{"id":84763,"date":"2026-06-24T17:27:12","date_gmt":"2026-06-24T17:27:12","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/84763\/"},"modified":"2026-06-24T17:27:12","modified_gmt":"2026-06-24T17:27:12","slug":"facilitating-structure-based-drug-discovery-with-an-artificial-intelligence-driven-virtual-screening-platform","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/84763\/","title":{"rendered":"Facilitating structure-based drug discovery with an artificial intelligence-driven virtual screening platform"},"content":{"rendered":"<p class=\"c-article-references__text\" id=\"ref-CR1\">Berdigaliyev, N. &amp; Aljofan, M. An overview of drug discovery and development. Future Med. Chem. 12, 939\u2013947 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.4155\/fmc-2019-0307\" data-track-item_id=\"10.4155\/fmc-2019-0307\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.4155%2Ffmc-2019-0307\" aria-label=\"Article reference 1\" data-doi=\"10.4155\/fmc-2019-0307\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3cXpslygt74%3D\" aria-label=\"CAS reference 1\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=32270704\" aria-label=\"PubMed reference 1\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 1\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=An%20overview%20of%20drug%20discovery%20and%20development&amp;journal=Future%20Med.%20Chem.&amp;doi=10.4155%2Ffmc-2019-0307&amp;volume=12&amp;pages=939-947&amp;publication_year=2020&amp;author=Berdigaliyev%2CN&amp;author=Aljofan%2CM\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR2\">Hughes, J. P., Rees, S., Kalindjian, S. B. &amp; Philpott, K. L. Principles of early drug discovery. Br. J. Pharmacol. 162, 1239\u20131249 (2011).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1111\/j.1476-5381.2010.01127.x\" data-track-item_id=\"10.1111\/j.1476-5381.2010.01127.x\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1111%2Fj.1476-5381.2010.01127.x\" aria-label=\"Article reference 2\" data-doi=\"10.1111\/j.1476-5381.2010.01127.x\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC3MXivF2lt7o%3D\" aria-label=\"CAS reference 2\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=21091654\" aria-label=\"PubMed reference 2\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC3058157\" aria-label=\"PubMed Central reference 2\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 2\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Principles%20of%20early%20drug%20discovery&amp;journal=Br.%20J.%20Pharmacol.&amp;doi=10.1111%2Fj.1476-5381.2010.01127.x&amp;volume=162&amp;pages=1239-1249&amp;publication_year=2011&amp;author=Hughes%2CJP&amp;author=Rees%2CS&amp;author=Kalindjian%2CSB&amp;author=Philpott%2CKL\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR3\">Jones, A. &amp; Clifford, L. Drug discovery alliances. Nat. Rev. Drug Discov. 4, 807\u2013808 (2005).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/nrd1856\" data-track-item_id=\"10.1038\/nrd1856\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fnrd1856\" aria-label=\"Article reference 3\" data-doi=\"10.1038\/nrd1856\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BD2MXhtV2kt7fP\" aria-label=\"CAS reference 3\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=16237845\" aria-label=\"PubMed reference 3\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 3\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Drug%20discovery%20alliances&amp;journal=Nat.%20Rev.%20Drug%20Discov.&amp;doi=10.1038%2Fnrd1856&amp;volume=4&amp;pages=807-808&amp;publication_year=2005&amp;author=Jones%2CA&amp;author=Clifford%2CL\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR4\">Zhu, T. et al. Hit identification and optimization in virtual screening: practical recommendations based on a critical literature analysis. J. Med. Chem. 56, 6560\u20136572 (2013).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1021\/jm301916b\" data-track-item_id=\"10.1021\/jm301916b\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1021%2Fjm301916b\" aria-label=\"Article reference 4\" data-doi=\"10.1021\/jm301916b\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC3sXnvFyis7o%3D\" aria-label=\"CAS reference 4\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=23688234\" aria-label=\"PubMed reference 4\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC3772997\" aria-label=\"PubMed Central reference 4\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 4\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Hit%20identification%20and%20optimization%20in%20virtual%20screening%3A%20practical%20recommendations%20based%20on%20a%20critical%20literature%20analysis&amp;journal=J.%20Med.%20Chem.&amp;doi=10.1021%2Fjm301916b&amp;volume=56&amp;pages=6560-6572&amp;publication_year=2013&amp;author=Zhu%2CT\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR5\">Bleicher, K. H., Bohm, H. J., Muller, K. &amp; Alanine, A. I. Hit and lead generation: beyond high-throughput screening. Nat. Rev. Drug Discov. 2, 369\u2013378 (2003).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/nrd1086\" data-track-item_id=\"10.1038\/nrd1086\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fnrd1086\" aria-label=\"Article reference 5\" data-doi=\"10.1038\/nrd1086\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BD3sXjslamtb8%3D\" aria-label=\"CAS reference 5\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=12750740\" aria-label=\"PubMed reference 5\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 5\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Hit%20and%20lead%20generation%3A%20beyond%20high-throughput%20screening&amp;journal=Nat.%20Rev.%20Drug%20Discov.&amp;doi=10.1038%2Fnrd1086&amp;volume=2&amp;pages=369-378&amp;publication_year=2003&amp;author=Bleicher%2CKH&amp;author=Bohm%2CHJ&amp;author=Muller%2CK&amp;author=Alanine%2CAI\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR6\">Inglese, J. et al. High-throughput screening assays for the identification of chemical probes. Nat. Chem. Biol. 3, 466\u2013479 (2007).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/nchembio.2007.17\" data-track-item_id=\"10.1038\/nchembio.2007.17\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fnchembio.2007.17\" aria-label=\"Article reference 6\" data-doi=\"10.1038\/nchembio.2007.17\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BD2sXnvFaqsro%3D\" aria-label=\"CAS reference 6\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=17637779\" aria-label=\"PubMed reference 6\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 6\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=High-throughput%20screening%20assays%20for%20the%20identification%20of%20chemical%20probes&amp;journal=Nat.%20Chem.%20Biol.&amp;doi=10.1038%2Fnchembio.2007.17&amp;volume=3&amp;pages=466-479&amp;publication_year=2007&amp;author=Inglese%2CJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR7\">Bajorath, J. Integration of virtual and high-throughput screening. Nat. Rev. Drug Discov. 1, 882\u2013894 (2002).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/nrd941\" data-track-item_id=\"10.1038\/nrd941\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fnrd941\" aria-label=\"Article reference 7\" data-doi=\"10.1038\/nrd941\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BD38XotleqtLY%3D\" aria-label=\"CAS reference 7\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=12415248\" aria-label=\"PubMed reference 7\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 7\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Integration%20of%20virtual%20and%20high-throughput%20screening&amp;journal=Nat.%20Rev.%20Drug%20Discov.&amp;doi=10.1038%2Fnrd941&amp;volume=1&amp;pages=882-894&amp;publication_year=2002&amp;author=Bajorath%2CJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR8\">Schneider, G. Virtual screening: an endless staircase? Nat. Rev. Drug Discov. 9, 273\u2013276 (2010).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/nrd3139\" data-track-item_id=\"10.1038\/nrd3139\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fnrd3139\" aria-label=\"Article reference 8\" data-doi=\"10.1038\/nrd3139\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC3cXktVGjur4%3D\" aria-label=\"CAS reference 8\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=20357802\" aria-label=\"PubMed reference 8\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 8\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Virtual%20screening%3A%20an%20endless%20staircase%3F&amp;journal=Nat.%20Rev.%20Drug%20Discov.&amp;doi=10.1038%2Fnrd3139&amp;volume=9&amp;pages=273-276&amp;publication_year=2010&amp;author=Schneider%2CG\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR9\">Lyu, J., Irwin, J. J. &amp; Shoichet, B. K. Modeling the expansion of virtual screening libraries. Nat. Chem. Biol. 19, 712\u2013718 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41589-022-01234-w\" data-track-item_id=\"10.1038\/s41589-022-01234-w\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41589-022-01234-w\" aria-label=\"Article reference 9\" data-doi=\"10.1038\/s41589-022-01234-w\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXhslKht70%3D\" aria-label=\"CAS reference 9\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=36646956\" aria-label=\"PubMed reference 9\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC10243288\" aria-label=\"PubMed Central reference 9\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 9\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Modeling%20the%20expansion%20of%20virtual%20screening%20libraries&amp;journal=Nat.%20Chem.%20Biol.&amp;doi=10.1038%2Fs41589-022-01234-w&amp;volume=19&amp;pages=712-718&amp;publication_year=2023&amp;author=Lyu%2CJ&amp;author=Irwin%2CJJ&amp;author=Shoichet%2CBK\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR10\">Deane, C. &amp; Mokaya, M. A virtual drug-screening approach to conquer huge chemical libraries. Nature 601, 322\u2013323 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/d41586-021-03682-1\" data-track-item_id=\"10.1038\/d41586-021-03682-1\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fd41586-021-03682-1\" aria-label=\"Article reference 10\" data-doi=\"10.1038\/d41586-021-03682-1\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB38XhtlOmtrY%3D\" aria-label=\"CAS reference 10\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=34912059\" aria-label=\"PubMed reference 10\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 10\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=A%20virtual%20drug-screening%20approach%20to%20conquer%20huge%20chemical%20libraries&amp;journal=Nature&amp;doi=10.1038%2Fd41586-021-03682-1&amp;volume=601&amp;pages=322-323&amp;publication_year=2022&amp;author=Deane%2CC&amp;author=Mokaya%2CM\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR11\">Shoichet, B. K. Virtual screening of chemical libraries. Nature 432, 862\u2013865 (2004).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/nature03197\" data-track-item_id=\"10.1038\/nature03197\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fnature03197\" aria-label=\"Article reference 11\" data-doi=\"10.1038\/nature03197\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BD2cXhtVOht7jK\" aria-label=\"CAS reference 11\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=15602552\" aria-label=\"PubMed reference 11\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC1360234\" aria-label=\"PubMed Central reference 11\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 11\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Virtual%20screening%20of%20chemical%20libraries&amp;journal=Nature&amp;doi=10.1038%2Fnature03197&amp;volume=432&amp;pages=862-865&amp;publication_year=2004&amp;author=Shoichet%2CBK\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR12\">Ripphausen, P., Nisius, B. &amp; Bajorath, J. State-of-the-art in ligand-based virtual screening. Drug Discov. Today 16, 372\u2013376 (2011).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1016\/j.drudis.2011.02.011\" data-track-item_id=\"10.1016\/j.drudis.2011.02.011\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1016%2Fj.drudis.2011.02.011\" aria-label=\"Article reference 12\" data-doi=\"10.1016\/j.drudis.2011.02.011\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC3MXmtVyru7g%3D\" aria-label=\"CAS reference 12\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=21349346\" aria-label=\"PubMed reference 12\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 12\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=State-of-the-art%20in%20ligand-based%20virtual%20screening&amp;journal=Drug%20Discov.%20Today&amp;doi=10.1016%2Fj.drudis.2011.02.011&amp;volume=16&amp;pages=372-376&amp;publication_year=2011&amp;author=Ripphausen%2CP&amp;author=Nisius%2CB&amp;author=Bajorath%2CJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR13\">Lionta, E., Spyrou, G., Vassilatis, D. K. &amp; Cournia, Z. Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr. Top Med. Chem. 14, 1923\u20131938 (2014).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.2174\/1568026614666140929124445\" data-track-item_id=\"10.2174\/1568026614666140929124445\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.2174%2F1568026614666140929124445\" aria-label=\"Article reference 13\" data-doi=\"10.2174\/1568026614666140929124445\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC2cXhvVGgt7fJ\" aria-label=\"CAS reference 13\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=25262799\" aria-label=\"PubMed reference 13\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC4443793\" aria-label=\"PubMed Central reference 13\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 13\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Structure-based%20virtual%20screening%20for%20drug%20discovery%3A%20principles%2C%20applications%20and%20recent%20advances&amp;journal=Curr.%20Top%20Med.%20Chem.&amp;doi=10.2174%2F1568026614666140929124445&amp;volume=14&amp;pages=1923-1938&amp;publication_year=2014&amp;author=Lionta%2CE&amp;author=Spyrou%2CG&amp;author=Vassilatis%2CDK&amp;author=Cournia%2CZ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR14\">Lyne, P. D. Structure-based virtual screening: an overview. Drug Discov. Today 7, 1047\u20131055 (2002).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1016\/S1359-6446(02)02483-2\" data-track-item_id=\"10.1016\/S1359-6446(02)02483-2\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1016%2FS1359-6446%2802%2902483-2\" aria-label=\"Article reference 14\" data-doi=\"10.1016\/S1359-6446(02)02483-2\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BD38XnsF2ksr0%3D\" aria-label=\"CAS reference 14\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=12546894\" aria-label=\"PubMed reference 14\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 14\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Structure-based%20virtual%20screening%3A%20an%20overview&amp;journal=Drug%20Discov.%20Today&amp;doi=10.1016%2FS1359-6446%2802%2902483-2&amp;volume=7&amp;pages=1047-1055&amp;publication_year=2002&amp;author=Lyne%2CPD\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR15\">Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583\u2013589 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41586-021-03819-2\" data-track-item_id=\"10.1038\/s41586-021-03819-2\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41586-021-03819-2\" aria-label=\"Article reference 15\" data-doi=\"10.1038\/s41586-021-03819-2\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3MXhvVaktrrL\" aria-label=\"CAS reference 15\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=34265844\" aria-label=\"PubMed reference 15\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC8371605\" aria-label=\"PubMed Central reference 15\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 15\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Highly%20accurate%20protein%20structure%20prediction%20with%20AlphaFold&amp;journal=Nature&amp;doi=10.1038%2Fs41586-021-03819-2&amp;volume=596&amp;pages=583-589&amp;publication_year=2021&amp;author=Jumper%2CJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR16\">Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493\u2013500 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41586-024-07487-w\" data-track-item_id=\"10.1038\/s41586-024-07487-w\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41586-024-07487-w\" aria-label=\"Article reference 16\" data-doi=\"10.1038\/s41586-024-07487-w\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXhtlSntbjJ\" aria-label=\"CAS reference 16\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38718835\" aria-label=\"PubMed reference 16\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC11168924\" aria-label=\"PubMed Central reference 16\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 16\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Accurate%20structure%20prediction%20of%20biomolecular%20interactions%20with%20AlphaFold%203&amp;journal=Nature&amp;doi=10.1038%2Fs41586-024-07487-w&amp;volume=630&amp;pages=493-500&amp;publication_year=2024&amp;author=Abramson%2CJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR17\">Burley, S. K. et al. RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence\/machine learning. Nucleic Acids Res. 51, D488\u2013D508 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1093\/nar\/gkac1077\" data-track-item_id=\"10.1093\/nar\/gkac1077\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1093%2Fnar%2Fgkac1077\" aria-label=\"Article reference 17\" data-doi=\"10.1093\/nar\/gkac1077\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXhsVWhtr7P\" aria-label=\"CAS reference 17\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=36420884\" aria-label=\"PubMed reference 17\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC9825554\" aria-label=\"PubMed Central reference 17\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 17\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=RCSB%20Protein%20Data%20Bank%20%28RCSB.org%29%3A%20delivery%20of%20experimentally-determined%20PDB%20structures%20alongside%20one%20million%20computed%20structure%20models%20of%20proteins%20from%20artificial%20intelligence%2Fmachine%20learning&amp;journal=Nucleic%20Acids%20Res.&amp;doi=10.1093%2Fnar%2Fgkac1077&amp;volume=51&amp;pages=D488-D508&amp;publication_year=2023&amp;author=Burley%2CSK\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR18\">Gu, S. et al. Evaluation of AlphaFold2 structures for hit identification across multiple scenarios. J. Chem. Inf. Model 64, 3630\u20133639 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1021\/acs.jcim.3c01976\" data-track-item_id=\"10.1021\/acs.jcim.3c01976\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1021%2Facs.jcim.3c01976\" aria-label=\"Article reference 18\" data-doi=\"10.1021\/acs.jcim.3c01976\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXosVCjsLs%3D\" aria-label=\"CAS reference 18\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38630855\" aria-label=\"PubMed reference 18\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 18\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Evaluation%20of%20AlphaFold2%20structures%20for%20hit%20identification%20across%20multiple%20scenarios&amp;journal=J.%20Chem.%20Inf.%20Model&amp;doi=10.1021%2Facs.jcim.3c01976&amp;volume=64&amp;pages=3630-3639&amp;publication_year=2024&amp;author=Gu%2CS\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR19\">Shen, C. et al. Boosting protein\u2013ligand binding pose prediction and virtual screening based on residue-atom distance likelihood potential and graph transformer. J. Med. Chem. 65, 10691\u201310706 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1021\/acs.jmedchem.2c00991\" data-track-item_id=\"10.1021\/acs.jmedchem.2c00991\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1021%2Facs.jmedchem.2c00991\" aria-label=\"Article reference 19\" data-doi=\"10.1021\/acs.jmedchem.2c00991\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB38XhvF2htrbO\" aria-label=\"CAS reference 19\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=35917397\" aria-label=\"PubMed reference 19\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 19\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Boosting%20protein%E2%80%93ligand%20binding%20pose%20prediction%20and%20virtual%20screening%20based%20on%20residue-atom%20distance%20likelihood%20potential%20and%20graph%20transformer&amp;journal=J.%20Med.%20Chem.&amp;doi=10.1021%2Facs.jmedchem.2c00991&amp;volume=65&amp;pages=10691-10706&amp;publication_year=2022&amp;author=Shen%2CC\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR20\">Zhang, X. et al. Efficient and accurate large library ligand docking with KarmaDock. Nat. Comput. Sci. 3, 789\u2013804 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s43588-023-00511-5\" data-track-item_id=\"10.1038\/s43588-023-00511-5\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs43588-023-00511-5\" aria-label=\"Article reference 20\" data-doi=\"10.1038\/s43588-023-00511-5\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXhvF2nt77E\" aria-label=\"CAS reference 20\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38177786\" aria-label=\"PubMed reference 20\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 20\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Efficient%20and%20accurate%20large%20library%20ligand%20docking%20with%20KarmaDock&amp;journal=Nat.%20Comput.%20Sci.&amp;doi=10.1038%2Fs43588-023-00511-5&amp;volume=3&amp;pages=789-804&amp;publication_year=2023&amp;author=Zhang%2CX\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR21\">Cai, H. et al. CarsiDock: a deep learning paradigm for accurate protein\u2013ligand docking and screening based on large-scale pre-training. Chem. Sci. 15, 1449\u20131471 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1039\/D3SC05552C\" data-track-item_id=\"10.1039\/D3SC05552C\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1039%2FD3SC05552C\" aria-label=\"Article reference 21\" data-doi=\"10.1039\/D3SC05552C\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXhsVGksQ%3D%3D\" aria-label=\"CAS reference 21\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38274053\" aria-label=\"PubMed reference 21\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 21\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=CarsiDock%3A%20a%20deep%20learning%20paradigm%20for%20accurate%20protein%E2%80%93ligand%20docking%20and%20screening%20based%20on%20large-scale%20pre-training&amp;journal=Chem.%20Sci.&amp;doi=10.1039%2FD3SC05552C&amp;volume=15&amp;pages=1449-1471&amp;publication_year=2024&amp;author=Cai%2CH\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR22\">Gu, S. et al. Benchmarking AI-powered docking methods from the perspective of virtual screening. Nat. Mach. Intell. 7, 509\u2013520 (2025).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s42256-025-00993-0\" data-track-item_id=\"10.1038\/s42256-025-00993-0\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs42256-025-00993-0\" aria-label=\"Article reference 22\" data-doi=\"10.1038\/s42256-025-00993-0\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 22\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Benchmarking%20AI-powered%20docking%20methods%20from%20the%20perspective%20of%20virtual%20screening&amp;journal=Nat.%20Mach.%20Intell.&amp;doi=10.1038%2Fs42256-025-00993-0&amp;volume=7&amp;pages=509-520&amp;publication_year=2025&amp;author=Gu%2CS\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR23\">Corso, G., St\u00e4rk, H., Jing, B., Barzilay, R. &amp; Jaakkola, T. Diffdock: diffusion steps, twists, and turns for molecular docking. In International Conference on Learning Representations (ICLR, 2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR24\">Li, Y., Li, L., Wang, S. &amp; Tang, X. EQUIBIND: a geometric deep learning-based protein\u2013ligand binding prediction method. Drug Discov. Ther 17, 363\u2013364 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.5582\/ddt.2023.01063\" data-track-item_id=\"10.5582\/ddt.2023.01063\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.5582%2Fddt.2023.01063\" aria-label=\"Article reference 24\" data-doi=\"10.5582\/ddt.2023.01063\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXnsl2ru74%3D\" aria-label=\"CAS reference 24\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=37766553\" aria-label=\"PubMed reference 24\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 24\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=EQUIBIND%3A%20a%20geometric%20deep%20learning-based%20protein%E2%80%93ligand%20binding%20prediction%20method&amp;journal=Drug%20Discov.%20Ther&amp;doi=10.5582%2Fddt.2023.01063&amp;volume=17&amp;pages=363-364&amp;publication_year=2023&amp;author=Li%2CY&amp;author=Li%2CL&amp;author=Wang%2CS&amp;author=Tang%2CX\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR25\">Lu, W. et al. TANKBind: trigonometry-aware neural networks for drug-protein binding structure prediction. Adv. Neural Inf. Process. Syst. 35, 7236\u20137249 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.52202\/068431-0525\" data-track-item_id=\"10.52202\/068431-0525\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.52202%2F068431-0525\" aria-label=\"Article reference 25\" data-doi=\"10.52202\/068431-0525\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 25\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=TANKBind%3A%20trigonometry-aware%20neural%20networks%20for%20drug-protein%20binding%20structure%20prediction&amp;journal=Adv.%20Neural%20Inf.%20Process.%20Syst.&amp;doi=10.52202%2F068431-0525&amp;volume=35&amp;pages=7236-7249&amp;publication_year=2022&amp;author=Lu%2CW\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR26\">Friesner, R. A. et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 47, 1739\u20131749 (2004).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1021\/jm0306430\" data-track-item_id=\"10.1021\/jm0306430\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1021%2Fjm0306430\" aria-label=\"Article reference 26\" data-doi=\"10.1021\/jm0306430\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BD2cXhsFyit74%3D\" aria-label=\"CAS reference 26\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=15027865\" aria-label=\"PubMed reference 26\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 26\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Glide%3A%20a%20new%20approach%20for%20rapid%2C%20accurate%20docking%20and%20scoring.%201.%20Method%20and%20assessment%20of%20docking%20accuracy&amp;journal=J.%20Med.%20Chem.&amp;doi=10.1021%2Fjm0306430&amp;volume=47&amp;pages=1739-1749&amp;publication_year=2004&amp;author=Friesner%2CRA\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR27\">Wang, Z. et al. Comprehensive evaluation of ten docking programs on a diverse set of protein\u2013ligand complexes: the prediction accuracy of sampling power and scoring power. Phys. Chem. Chem. Phys. 18, 12964\u201312975 (2016).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1039\/C6CP01555G\" data-track-item_id=\"10.1039\/C6CP01555G\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1039%2FC6CP01555G\" aria-label=\"Article reference 27\" data-doi=\"10.1039\/C6CP01555G\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC28XmvFOns7g%3D\" aria-label=\"CAS reference 27\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=27108770\" aria-label=\"PubMed reference 27\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 27\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Comprehensive%20evaluation%20of%20ten%20docking%20programs%20on%20a%20diverse%20set%20of%20protein%E2%80%93ligand%20complexes%3A%20the%20prediction%20accuracy%20of%20sampling%20power%20and%20scoring%20power&amp;journal=Phys.%20Chem.%20Chem.%20Phys.&amp;doi=10.1039%2FC6CP01555G&amp;volume=18&amp;pages=12964-12975&amp;publication_year=2016&amp;author=Wang%2CZ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR28\">Ruiz-Carmona, S. et al. rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids. PLoS Comput. Biol. 10, e1003571 (2014).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1371\/journal.pcbi.1003571\" data-track-item_id=\"10.1371\/journal.pcbi.1003571\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1371%2Fjournal.pcbi.1003571\" aria-label=\"Article reference 28\" data-doi=\"10.1371\/journal.pcbi.1003571\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=24722481\" aria-label=\"PubMed reference 28\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC3983074\" aria-label=\"PubMed Central reference 28\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 28\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=rDock%3A%20a%20fast%2C%20versatile%20and%20open%20source%20program%20for%20docking%20ligands%20to%20proteins%20and%20nucleic%20acids&amp;journal=PLoS%20Comput.%20Biol.&amp;doi=10.1371%2Fjournal.pcbi.1003571&amp;volume=10&amp;publication_year=2014&amp;author=Ruiz-Carmona%2CS\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR29\">Jain, A. N. Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J. Med. Chem. 46, 499\u2013511 (2003).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1021\/jm020406h\" data-track-item_id=\"10.1021\/jm020406h\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1021%2Fjm020406h\" aria-label=\"Article reference 29\" data-doi=\"10.1021\/jm020406h\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BD3sXkvFKjsA%3D%3D\" aria-label=\"CAS reference 29\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=12570372\" aria-label=\"PubMed reference 29\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 29\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Surflex%3A%20fully%20automatic%20flexible%20molecular%20docking%20using%20a%20molecular%20similarity-based%20search%20engine&amp;journal=J.%20Med.%20Chem.&amp;doi=10.1021%2Fjm020406h&amp;volume=46&amp;pages=499-511&amp;publication_year=2003&amp;author=Jain%2CAN\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR30\">Xia, S., Chen, E. &amp; Zhang, Y. Integrated molecular modeling and machine learning for drug design. J. Chem. Theory Comput. 19, 7478\u20137495 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1021\/acs.jctc.3c00814\" data-track-item_id=\"10.1021\/acs.jctc.3c00814\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1021%2Facs.jctc.3c00814\" aria-label=\"Article reference 30\" data-doi=\"10.1021\/acs.jctc.3c00814\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXit1ShtrzL\" aria-label=\"CAS reference 30\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=37883810\" aria-label=\"PubMed reference 30\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC10653122\" aria-label=\"PubMed Central reference 30\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 30\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Integrated%20molecular%20modeling%20and%20machine%20learning%20for%20drug%20design&amp;journal=J.%20Chem.%20Theory%20Comput.&amp;doi=10.1021%2Facs.jctc.3c00814&amp;volume=19&amp;pages=7478-7495&amp;publication_year=2023&amp;author=Xia%2CS&amp;author=Chen%2CE&amp;author=Zhang%2CY\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR31\">Wishart, D. S. et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34, D668\u2013D672 (2006).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1093\/nar\/gkj067\" data-track-item_id=\"10.1093\/nar\/gkj067\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1093%2Fnar%2Fgkj067\" aria-label=\"Article reference 31\" data-doi=\"10.1093\/nar\/gkj067\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BD28XisFOrsw%3D%3D\" aria-label=\"CAS reference 31\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=16381955\" aria-label=\"PubMed reference 31\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC1347430\" aria-label=\"PubMed Central reference 31\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 31\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=DrugBank%3A%20a%20comprehensive%20resource%20for%20in%20silico%20drug%20discovery%20and%20exploration&amp;journal=Nucleic%20Acids%20Res.&amp;doi=10.1093%2Fnar%2Fgkj067&amp;volume=34&amp;pages=D668-D672&amp;publication_year=2006&amp;author=Wishart%2CDS\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR32\">Chi, X. et al. Discovery and characterization of novel FAK inhibitors for breast cancer therapy via hybrid virtual screening, biological evaluation and molecular dynamics simulations. Bioorg. Chem. 159, 108400 (2025).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1016\/j.bioorg.2025.108400\" data-track-item_id=\"10.1016\/j.bioorg.2025.108400\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1016%2Fj.bioorg.2025.108400\" aria-label=\"Article reference 32\" data-doi=\"10.1016\/j.bioorg.2025.108400\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2MXnsFOjs7c%3D\" aria-label=\"CAS reference 32\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=40163988\" aria-label=\"PubMed reference 32\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 32\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Discovery%20and%20characterization%20of%20novel%20FAK%20inhibitors%20for%20breast%20cancer%20therapy%20via%20hybrid%20virtual%20screening%2C%20biological%20evaluation%20and%20molecular%20dynamics%20simulations&amp;journal=Bioorg.%20Chem.&amp;doi=10.1016%2Fj.bioorg.2025.108400&amp;volume=159&amp;publication_year=2025&amp;author=Chi%2CX\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR33\">Passaro, S. et al. Boltz-2: towards accurate and efficient binding affinity prediction. Preprint at bioRxiv <a href=\"https:\/\/doi.org\/10.1101\/2025.06.14.659707\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"10.1101\/2025.06.14.659707\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/doi.org\/10.1101\/2025.06.14.659707<\/a> (2025).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR34\">Team, B. A. A. S. et al. Protenix\u2014advancing structure prediction through a comprehensive AlphaFold3 reproduction. Preprint at bioRxiv <a href=\"https:\/\/doi.org\/10.1101\/2025.01.08.631967\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"10.1101\/2025.01.08.631967\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/doi.org\/10.1101\/2025.01.08.631967<\/a> (2025).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR35\">Bauer, M. R., Ibrahim, T. M., Vogel, S. M. &amp; Boeckler, F. M. Evaluation and optimization of virtual screening workflows with DEKOIS 2.0\u2014a public library of challenging docking benchmark sets. J. Chem. Inf. Model. 53, 1447\u20131462 (2013).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1021\/ci400115b\" data-track-item_id=\"10.1021\/ci400115b\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1021%2Fci400115b\" aria-label=\"Article reference 35\" data-doi=\"10.1021\/ci400115b\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC3sXot1SqsrY%3D\" aria-label=\"CAS reference 35\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=23705874\" aria-label=\"PubMed reference 35\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 35\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Evaluation%20and%20optimization%20of%20virtual%20screening%20workflows%20with%20DEKOIS%202.0%E2%80%94a%20public%20library%20of%20challenging%20docking%20benchmark%20sets&amp;journal=J.%20Chem.%20Inf.%20Model.&amp;doi=10.1021%2Fci400115b&amp;volume=53&amp;pages=1447-1462&amp;publication_year=2013&amp;author=Bauer%2CMR&amp;author=Ibrahim%2CTM&amp;author=Vogel%2CSM&amp;author=Boeckler%2CFM\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR36\">Chen, L. et al. Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening. PLoS ONE 14, e0220113 (2019).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1371\/journal.pone.0220113\" data-track-item_id=\"10.1371\/journal.pone.0220113\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1371%2Fjournal.pone.0220113\" aria-label=\"Article reference 36\" data-doi=\"10.1371\/journal.pone.0220113\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC1MXhvFeks7rL\" aria-label=\"CAS reference 36\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=31430292\" aria-label=\"PubMed reference 36\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC6701836\" aria-label=\"PubMed Central reference 36\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 36\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Hidden%20bias%20in%20the%20DUD-E%20dataset%20leads%20to%20misleading%20performance%20of%20deep%20learning%20in%20structure-based%20virtual%20screening&amp;journal=PLoS%20ONE&amp;doi=10.1371%2Fjournal.pone.0220113&amp;volume=14&amp;publication_year=2019&amp;author=Chen%2CL\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR37\">Chatterjee, A. et al. Improving the generalizability of protein\u2013ligand binding predictions with AI-Bind. Nat. Commun. 14, 1989 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41467-023-37572-z\" data-track-item_id=\"10.1038\/s41467-023-37572-z\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41467-023-37572-z\" aria-label=\"Article reference 37\" data-doi=\"10.1038\/s41467-023-37572-z\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXnslWks78%3D\" aria-label=\"CAS reference 37\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=37031187\" aria-label=\"PubMed reference 37\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC10082765\" aria-label=\"PubMed Central reference 37\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 37\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Improving%20the%20generalizability%20of%20protein%E2%80%93ligand%20binding%20predictions%20with%20AI-Bind&amp;journal=Nat.%20Commun.&amp;doi=10.1038%2Fs41467-023-37572-z&amp;volume=14&amp;publication_year=2023&amp;author=Chatterjee%2CA\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR38\">Mohammad, T., Mathur, Y. &amp; Hassan, M. I. InstaDock: a single-click graphical user interface for molecular docking-based virtual high-throughput screening. Brief. Bioinform. 22, <a href=\"https:\/\/doi.org\/10.1093\/bib\/bbaa279\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"10.1093\/bib\/bbaa279\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/doi.org\/10.1093\/bib\/bbaa279<\/a> (2021).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR39\">Mo, Q., Xu, Z., Yan, H., Chen, P. &amp; Lu, Y. VSTH: a user-friendly web server for structure-based virtual screening on Tianhe-2. Bioinformatics <a href=\"https:\/\/doi.org\/10.1093\/bioinformatics\/btac740\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"10.1093\/bioinformatics\/btac740\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/doi.org\/10.1093\/bioinformatics\/btac740<\/a> (2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR40\">Gan, J. H. et al. DrugRep: an automatic virtual screening server for drug repurposing. Acta Pharmacol. Sin. 44, 888\u2013896 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41401-022-00996-2\" data-track-item_id=\"10.1038\/s41401-022-00996-2\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41401-022-00996-2\" aria-label=\"Article reference 40\" data-doi=\"10.1038\/s41401-022-00996-2\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB38XisF2lsrvK\" aria-label=\"CAS reference 40\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=36216900\" aria-label=\"PubMed reference 40\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 40\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=DrugRep%3A%20an%20automatic%20virtual%20screening%20server%20for%20drug%20repurposing&amp;journal=Acta%20Pharmacol.%20Sin.&amp;doi=10.1038%2Fs41401-022-00996-2&amp;volume=44&amp;pages=888-896&amp;publication_year=2023&amp;author=Gan%2CJH\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR41\">Guedes, I. A. et al. DockThor-VS: a free platform for receptor-ligand virtual screening. J. Mol. Biol. 436, 168548 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1016\/j.jmb.2024.168548\" data-track-item_id=\"10.1016\/j.jmb.2024.168548\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1016%2Fj.jmb.2024.168548\" aria-label=\"Article reference 41\" data-doi=\"10.1016\/j.jmb.2024.168548\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXns1Kqsbg%3D\" aria-label=\"CAS reference 41\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=39237203\" aria-label=\"PubMed reference 41\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 41\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=DockThor-VS%3A%20a%20free%20platform%20for%20receptor-ligand%20virtual%20screening&amp;journal=J.%20Mol.%20Biol.&amp;doi=10.1016%2Fj.jmb.2024.168548&amp;volume=436&amp;publication_year=2024&amp;author=Guedes%2CIA\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR42\">Zhou, G. et al. An artificial intelligence accelerated virtual screening platform for drug discovery. Nat. Commun. 15, 7761 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41467-024-52061-7\" data-track-item_id=\"10.1038\/s41467-024-52061-7\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41467-024-52061-7\" aria-label=\"Article reference 42\" data-doi=\"10.1038\/s41467-024-52061-7\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXhvFemsbbM\" aria-label=\"CAS reference 42\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=39237523\" aria-label=\"PubMed reference 42\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC11377542\" aria-label=\"PubMed Central reference 42\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 42\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=An%20artificial%20intelligence%20accelerated%20virtual%20screening%20platform%20for%20drug%20discovery&amp;journal=Nat.%20Commun.&amp;doi=10.1038%2Fs41467-024-52061-7&amp;volume=15&amp;publication_year=2024&amp;author=Zhou%2CG\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR43\">Gao, B. et al. DrugCLIP: contrastive protein-molecule representation learning for virtual screening. Adv Neural Inf. Process. Syst. 36, 44595\u201344614 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 43\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=DrugCLIP%3A%20contrastive%20protein-molecule%20representation%20learning%20for%20virtual%20screening&amp;journal=Adv%20Neural%20Inf.%20Process.%20Syst.&amp;volume=36&amp;pages=44595-44614&amp;publication_year=2024&amp;author=Gao%2CB\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR44\">Buttenschoen, M., Morris, G. M. &amp; Deane, C. M. PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chem. Sci. 15, 3130\u20133139 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1039\/D3SC04185A\" data-track-item_id=\"10.1039\/D3SC04185A\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1039%2FD3SC04185A\" aria-label=\"Article reference 44\" data-doi=\"10.1039\/D3SC04185A\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXisF2ms7vF\" aria-label=\"CAS reference 44\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38425520\" aria-label=\"PubMed reference 44\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 44\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=PoseBusters%3A%20AI-based%20docking%20methods%20fail%20to%20generate%20physically%20valid%20poses%20or%20generalise%20to%20novel%20sequences&amp;journal=Chem.%20Sci.&amp;doi=10.1039%2FD3SC04185A&amp;volume=15&amp;pages=3130-3139&amp;publication_year=2024&amp;author=Buttenschoen%2CM&amp;author=Morris%2CGM&amp;author=Deane%2CCM\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR45\">Masters, M. R., Mahmoud, A. H. &amp; Lill, M. A. Investigating whether deep learning models for co-folding learn the physics of protein\u2013ligand interactions. Nat. Commun. 16, 8854 (2025).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41467-025-63947-5\" data-track-item_id=\"10.1038\/s41467-025-63947-5\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41467-025-63947-5\" aria-label=\"Article reference 45\" data-doi=\"10.1038\/s41467-025-63947-5\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2MXisV2nu7rN\" aria-label=\"CAS reference 45\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=41053181\" aria-label=\"PubMed reference 45\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC12501370\" aria-label=\"PubMed Central reference 45\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 45\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Investigating%20whether%20deep%20learning%20models%20for%20co-folding%20learn%20the%20physics%20of%20protein%E2%80%93ligand%20interactions&amp;journal=Nat.%20Commun.&amp;doi=10.1038%2Fs41467-025-63947-5&amp;volume=16&amp;publication_year=2025&amp;author=Masters%2CMR&amp;author=Mahmoud%2CAH&amp;author=Lill%2CMA\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR46\">Li, Y. et al. Decoding the limits of deep learning in molecular docking for drug discovery. Chem. Sci. 16, 17374\u201317390 (2025).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1039\/D5SC05395A\" data-track-item_id=\"10.1039\/D5SC05395A\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1039%2FD5SC05395A\" aria-label=\"Article reference 46\" data-doi=\"10.1039\/D5SC05395A\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2MXitFGqtr7L\" aria-label=\"CAS reference 46\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=40901622\" aria-label=\"PubMed reference 46\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC12401186\" aria-label=\"PubMed Central reference 46\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 46\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Decoding%20the%20limits%20of%20deep%20learning%20in%20molecular%20docking%20for%20drug%20discovery&amp;journal=Chem.%20Sci.&amp;doi=10.1039%2FD5SC05395A&amp;volume=16&amp;pages=17374-17390&amp;publication_year=2025&amp;author=Li%2CY\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR47\">Landrum, G. et al. RDKit: open-source cheminformatics. GitHub and SourceForge <a href=\"https:\/\/www.rdkit.org\/\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/www.rdkit.org\/\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/www.rdkit.org\/<\/a> (2025).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR48\">Tran-Nguyen, V. K., Jacquemard, C. &amp; Rognan, D. LIT-PCBA: an unbiased data set for machine learning and virtual screening. J. Chem. Inf. Model. 60, 4263\u20134273 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1021\/acs.jcim.0c00155\" data-track-item_id=\"10.1021\/acs.jcim.0c00155\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1021%2Facs.jcim.0c00155\" aria-label=\"Article reference 48\" data-doi=\"10.1021\/acs.jcim.0c00155\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3cXntVKntr8%3D\" aria-label=\"CAS reference 48\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=32282202\" aria-label=\"PubMed reference 48\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 48\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=LIT-PCBA%3A%20an%20unbiased%20data%20set%20for%20machine%20learning%20and%20virtual%20screening&amp;journal=J.%20Chem.%20Inf.%20Model.&amp;doi=10.1021%2Facs.jcim.0c00155&amp;volume=60&amp;pages=4263-4273&amp;publication_year=2020&amp;author=Tran-Nguyen%2CVK&amp;author=Jacquemard%2CC&amp;author=Rognan%2CD\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR49\">Sastry, G. M., Adzhigirey, M., Day, T., Annabhimoju, R. &amp; Sherman, W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des. 27, 221\u2013234 (2013).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"noopener nofollow\" data-track-label=\"10.1007\/s10822-013-9644-8\" data-track-item_id=\"10.1007\/s10822-013-9644-8\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/link.springer.com\/doi\/10.1007\/s10822-013-9644-8\" aria-label=\"Article reference 49\" data-doi=\"10.1007\/s10822-013-9644-8\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=23579614\" aria-label=\"PubMed reference 49\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 49\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Protein%20and%20ligand%20preparation%3A%20parameters%2C%20protocols%2C%20and%20influence%20on%20virtual%20screening%20enrichments&amp;journal=J.%20Comput.%20Aided%20Mol.%20Des.&amp;doi=10.1007%2Fs10822-013-9644-8&amp;volume=27&amp;pages=221-234&amp;publication_year=2013&amp;author=Sastry%2CGM&amp;author=Adzhigirey%2CM&amp;author=Day%2CT&amp;author=Annabhimoju%2CR&amp;author=Sherman%2CW\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR50\">Gu, S. The Docker image for \u2018CVSP-AIE: a comprehensive virtual screening platform with artificial intelligence engine\u2019. Zenodo <a href=\"https:\/\/zenodo.org\/records\/18073542\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/zenodo.org\/records\/18073542\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/zenodo.org\/records\/18073542<\/a> (2025).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR51\">Gu, S. Code for \u2018Facilitating structure-based drug discovery with an artificial intelligence driven virtual screening platform\u2019. Zenodo <a href=\"https:\/\/zenodo.org\/records\/19448262\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/zenodo.org\/records\/19448262\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/zenodo.org\/records\/19448262<\/a> (2026).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR52\">Chen, L. et al. TransformerCPI: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments. Bioinformatics 36, 4406\u20134414 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1093\/bioinformatics\/btaa524\" data-track-item_id=\"10.1093\/bioinformatics\/btaa524\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1093%2Fbioinformatics%2Fbtaa524\" aria-label=\"Article reference 52\" data-doi=\"10.1093\/bioinformatics\/btaa524\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3MXivVKjsbg%3D\" aria-label=\"CAS reference 52\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=32428219\" aria-label=\"PubMed reference 52\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 52\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=TransformerCPI%3A%20improving%20compound-protein%20interaction%20prediction%20by%20sequence-based%20deep%20learning%20with%20self-attention%20mechanism%20and%20label%20reversal%20experiments&amp;journal=Bioinformatics&amp;doi=10.1093%2Fbioinformatics%2Fbtaa524&amp;volume=36&amp;pages=4406-4414&amp;publication_year=2020&amp;author=Chen%2CL\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR53\">Eberhardt, J., Santos-Martins, D., Tillack, A. F. &amp; Forli, S. AutoDock Vina 1.2.0: new docking methods, expanded force field, and python bindings. J. Chem. Inf. Model. 61, 3891\u20133898 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1021\/acs.jcim.1c00203\" data-track-item_id=\"10.1021\/acs.jcim.1c00203\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1021%2Facs.jcim.1c00203\" aria-label=\"Article reference 53\" data-doi=\"10.1021\/acs.jcim.1c00203\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3MXhsFGqtb%2FP\" aria-label=\"CAS reference 53\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=34278794\" aria-label=\"PubMed reference 53\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC10683950\" aria-label=\"PubMed Central reference 53\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 53\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=AutoDock%20Vina%201.2.0%3A%20new%20docking%20methods%2C%20expanded%20force%20field%2C%20and%20python%20bindings&amp;journal=J.%20Chem.%20Inf.%20Model.&amp;doi=10.1021%2Facs.jcim.1c00203&amp;volume=61&amp;pages=3891-3898&amp;publication_year=2021&amp;author=Eberhardt%2CJ&amp;author=Santos-Martins%2CD&amp;author=Tillack%2CAF&amp;author=Forli%2CS\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR54\">Verdonk, M. L., Cole, J. C., Hartshorn, M. J., Murray, C. W. &amp; Taylor, R. D. Improved protein\u2013ligand docking using GOLD. Proteins 52, 609\u2013623 (2003).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1002\/prot.10465\" data-track-item_id=\"10.1002\/prot.10465\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1002%2Fprot.10465\" aria-label=\"Article reference 54\" data-doi=\"10.1002\/prot.10465\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BD3sXmvFGrsLg%3D\" aria-label=\"CAS reference 54\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=12910460\" aria-label=\"PubMed reference 54\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 54\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Improved%20protein%E2%80%93ligand%20docking%20using%20GOLD&amp;journal=Proteins&amp;doi=10.1002%2Fprot.10465&amp;volume=52&amp;pages=609-623&amp;publication_year=2003&amp;author=Verdonk%2CML&amp;author=Cole%2CJC&amp;author=Hartshorn%2CMJ&amp;author=Murray%2CCW&amp;author=Taylor%2CRD\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR55\">Santos-Martins, D. et al. Accelerating AutoDock4 with GPUs and gradient-based local search. J. Chem. Theory Comput. 17, 1060\u20131073 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1021\/acs.jctc.0c01006\" data-track-item_id=\"10.1021\/acs.jctc.0c01006\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1021%2Facs.jctc.0c01006\" aria-label=\"Article reference 55\" data-doi=\"10.1021\/acs.jctc.0c01006\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3MXislWgsA%3D%3D\" aria-label=\"CAS reference 55\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=33403848\" aria-label=\"PubMed reference 55\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC8063785\" aria-label=\"PubMed Central reference 55\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 55\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Accelerating%20AutoDock4%20with%20GPUs%20and%20gradient-based%20local%20search&amp;journal=J.%20Chem.%20Theory%20Comput.&amp;doi=10.1021%2Facs.jctc.0c01006&amp;volume=17&amp;pages=1060-1073&amp;publication_year=2021&amp;author=Santos-Martins%2CD\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR56\">Fey, M. &amp; Lenssen, J. E. Fast graph representation learning with PyTorch Geometric. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/1903.02428\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/arxiv.org\/abs\/1903.02428\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/arxiv.org\/abs\/1903.02428<\/a> (2019).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR57\">Paszke, A. et al. Pytorch: an imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32 (eds Wallach, H. M. et al.) 8026\u20138037 (ACM, 2019).<\/p>\n","protected":false},"excerpt":{"rendered":"Berdigaliyev, N. &amp; Aljofan, M. An overview of drug discovery and development. Future Med. Chem. 12, 939\u2013947 (2020).&hellip;\n","protected":false},"author":2,"featured_media":84764,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[24,45681,25,2570,39002,45683,617,11183,45682,44731,136,45679,45680],"class_list":["post-84763","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai","tag-ai","tag-analytical-chemistry","tag-artificial-intelligence","tag-biological-techniques","tag-cheminformatics","tag-computational-biology-bioinformatics","tag-general","tag-life-sciences","tag-microarrays","tag-organic-chemistry","tag-software","tag-virtual-drug-screening","tag-virtual-screening"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/84763","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=84763"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/84763\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/84764"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=84763"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=84763"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=84763"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}