{"id":70283,"date":"2026-06-11T12:22:24","date_gmt":"2026-06-11T12:22:24","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/70283\/"},"modified":"2026-06-11T12:22:24","modified_gmt":"2026-06-11T12:22:24","slug":"bridging-three-dimensional-molecular-structures-and-artificial-intelligence-with-a-conformation-description-language","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/70283\/","title":{"rendered":"Bridging three-dimensional molecular structures and artificial intelligence with a conformation description language"},"content":{"rendered":"

Xu, Y. et al. Artificial intelligence: a powerful paradigm for scientific research. Innovation 2, 100179 (2021).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, H., Li, J., Wu, H., Hovy, E. & Sun, Y. Pre-trained language models and their applications. Engineering 25, 51\u201365 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Chang, Y. et al. A survey on evaluation of large language models. ACM Trans. Intell. Syst. Technol. 15, 39 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Burton, J. W. et al. How large language models can reshape collective intelligence. Nat. Hum. Behav. 8, 1643\u20131655 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Demszky, D. et al. Using large language models in psychology. Nat. Rev. Psychol. 2, 688\u2013701 (2023).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

OpenAI. GPT-4 technical report (2023); https:\/\/cdn.openai.com\/papers\/gpt-4.pdf<\/a><\/p>\n

Google Team et al. Gemini: a family of highly capable multimodal models. Preprint at https:\/\/arxiv.org\/abs\/2312.11805<\/a> (2023).<\/p>\n

Hayes, T. et al. Simulating 500 million years of evolution with a language model. Science 387, 850\u2013858 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123\u20131130 (2023).<\/p>\n

Article<\/a>\u00a0
\n MathSciNet<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618, 616\u2013624 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Dalla-Torre, H. et al. Nucleotide Transformer: building and evaluating robust foundation models for human genomics. Nat. Methods 22, 287\u2013297 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Chen, B. et al. xTrimoPGLM: unified 100-billion-parameter pretrained transformer for deciphering the language of proteins. Nat. Methods 22, 1028\u20131039 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Nguyen, E. et al. Sequence modeling and design from molecular to genome scale with Evo. Science 386, eado9336 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lu, J. & Zhang, Y. Unified deep learning model for multitask reaction predictions with explanation. J. Chem. Inf. Model. 62, 1376\u20131387 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Born, J. & Manica, M. Regression Transformer enables concurrent sequence regression and generation for molecular language modelling. Nat. Mach. Intell. 5, 432\u2013444 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Chen, S. & Zhong, F. GPCRSPACE: a new GPCR real expanded library based on large language models architecture and positive sample machine learning strategies. J. Med. Chem. 67, 16912\u201316922 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhong, Z. et al. Root-aligned SMILES: a tight representation for chemical reaction prediction. Chem. Sci. 13, 9023\u20139034 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Xiong, J. et al. \u03b1Extractor: a system for automatic extraction of chemical information from biomedical literature. Sci. China Life Sci. 67, 618\u2013621 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bagal, V., Aggarwal, R., Vinod, P. K. & Priyakumar, U. D. MolGPT: molecular generation using a transformer-decoder model. J. Chem. Inf. Model. 62, 2064\u20132076 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ross, J. et al. Large-scale chemical language representations capture molecular structure and properties. Nat. Mach. Intell. 4, 1256\u20131264 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Xiong, J. et al. Bridging chemistry and artificial intelligence by a reaction description language. Nat. Mach. Intell. 7, 782\u2013793 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Xu, F. et al. Toward a unified benchmark and framework for deep learning-based prediction of nuclear magnetic resonance chemical shifts. Nat. Comput. Sci. 5, 292\u2013300 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Nippa, D. F. et al. Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning. Nat. Chem. 16, 239\u2013248 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Luo, Y., Liu, Y. & Peng, J. Calibrated geometric deep learning improves kinase\u2013drug binding predictions. Nat. Mach. Intell. 5, 1390\u20131401 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhou, G. et al. Uni-Mol: a universal 3D molecular representation learning framework. In Proc. Eleventh International Conference on Learning Representations (ICLR, 2023).<\/p>\n

Li, S. et al. Towards 3D molecule-text interpretation in language models. In Proc. Twelfth International Conference on Learning Representations (ICLR, 2024).<\/p>\n

Jing, B., Corso, G., Chang, J., Barzilay, R. & Jaakkola, T. S. Torsional diffusion for molecular conformer generation. Adv. Neural Inf. Process. Syst. 35, 24240\u201324253 (2022).<\/p>\n

Corso, G., St\u00e4rk, H., Jing, B., Barzilay, R. & Jaakkola, T. S. DiffDock: diffusion steps, twists, and turns for molecular docking. In Proc. Eleventh International Conference on Learning Representations (ICLR, 2023).<\/p>\n

Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493\u2013500 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Alakhdar, A., Poczos, B. & Washburn, N. Diffusion models in de novo drug design. J. Chem. Inf. Model. 64, 7238\u20137256 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Feng, W. et al. Generation of 3D molecules in pockets via a language model. Nat. Mach. Intell. 6, 62\u201373 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, J. et al. 3DSMILES-GPT: 3D molecular pocket-based generation with token-only large language model. Chem. Sci. 16, 637\u2013648 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zholus, A. et al. BindGPT: a scalable framework for 3D molecular design via language modeling and reinforcement learning. In Proc. AAAI Conference on Artificial Intelligence Vol 39, 26083\u201326091 (AAAI Press, 2025).<\/p>\n

Qian, J., Wang, H., Li, Z., Li, S. & Yan, X. Limitations of language models in arithmetic and symbolic induction. In Proc. 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (eds Rogers, A. et al.) 9285\u20139298 (Association for Computational Linguistics, 2023).<\/p>\n

Zhang, W. et al. Interpreting and improving large language models in arithmetic calculation. In Proc. 41st International Conference on Machine Learning (eds Salakhutdinov, R. et al.) Vol 235, 59932\u201359950 (PMLR, 2024).<\/p>\n

Ganea, O. et al. GeoMol: torsional geometric generation of molecular 3D conformer ensembles. Adv. Neural Inf. Process. Syst. 34, 13757\u201313769 (2021).<\/p>\n

Xu, M. et al. GeoDiff: a geometric diffusion model for molecular conformation generation. In Proc. Tenth International Conference on Learning Representations (ICLR, 2022).<\/p>\n

Zhang, Z. et al. Tora3D: an autoregressive torsion angle prediction model for molecular 3D conformation generation. J. Cheminform. 15, 57 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Axelrod, S. & G\u00f3mez-Bombarelli, R. GEOM, energy-annotated molecular conformations for property prediction and molecular generation. Sci. Data 9, 185 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Isert, C., Atz, K., Jim\u00e9nez-Luna, J. & Schneider, G. QMugs, quantum mechanical properties of drug-like molecules. Sci. Data 9, 273 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhu, J. et al. Direct molecular conformation generation. Trans. Mach. Learn. Res. https:\/\/openreview.net\/forum?id=lCPOHiztuw<\/a> (2022).<\/p>\n

Wang, D., Dong, X., Zhang, X. & Hu, L. GADIFF: a transferable graph attention diffusion model for generating molecular conformations. Brief. Bioinform. 26, bbae676 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Preuer, K., Renz, P., Unterthiner, T., Hochreiter, S. & Klambauer, G. Fr\u00e9chet ChemNet distance: a metric for generative models for molecules in drug discovery. J. Chem. Inf. Model. 58, 1736\u20131741 (2018).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Buttenschoen, M., Morris, G. M. & 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

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Axen, S. D. et al. A simple representation of three-dimensional molecular structure. J. Med. Chem. 60, 7393\u20137409 (2017).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Morehead, A. & Cheng, J. Geometry-complete diffusion for 3D molecule generation and optimization. Commun. Chem. 7, 150 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zdrazil, B. et al. The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Res. 52, D1180\u2013D1192 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, Z., Yang, L. & Xiang, Z. RISurConv: rotation invariant surface attention-augmented convolutions for 3D point cloud classification and segmentation. In Computer Vision\u2014ECCV 2024: 18th European Conference Part XXVIII (eds Leonardis, A. et al.) Vol 15086, 93\u2013109 (Springer, 2024).<\/p>\n

Chen, Z., Peng, B., Zhai, T., Adu-Ampratwum, D. & Ning, X. Generating 3D small binding molecules using shape-conditioned diffusion models with guidance. Nat. Mach. Intell. 7, 758\u2013770 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Adams, K. & Coley, C. W. Equivariant shape-conditioned generation of 3D molecules for ligand-based drug design. In Proc. Eleventh International Conference on Learning Representations (ICLR, 2023).<\/p>\n

Liu, T. et al. BindingDB in 2024: a FAIR knowledgebase of protein-small molecule binding data. Nucleic Acids Res. 53, D1633\u2013D1644 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Goodsell, D. S. et al. RCSB Protein Data Bank: enabling biomedical research and drug discovery. Protein Sci. 29, 52\u201365 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sz\u00e9kely, G. J., Rizzo, M. L. & Bakirov, N. K. Measuring and testing dependence by correlation of distances. Ann. Stat. 35, 2769\u20132794 (2007).<\/p>\n

Article<\/a>\u00a0
\n MathSciNet<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mysinger, M. M., Carchia, M., Irwin, J. J. & Shoichet, B. K. Directory of Useful Decoys, Enhanced (DUD-E): better ligands and decoys for better benchmarking. J. Med. Chem. 55, 6582\u20136594 (2012).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tran-Nguyen, V.-K., Jacquemard, C. & Rognan, D. LIT-PCBA: an unbiased data set for machine learning and virtual screening. J. Chem. Inf. Model. 60, 4263\u20134273 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Devinyak, O., Havrylyuk, D. & Lesyk, R. 3D-MoRSE descriptors explained. J. Mol. Graph. Model. 54, 194\u2013203 (2014).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hu, J., Liu, Z., Yu, D.-J. & Zhang, Y. LS-align: an atom-level, flexible ligand structural alignment algorithm for high-throughput virtual screening. Bioinformatics 34, 2209\u20132218 (2018).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Liu, X., Jiang, H. & Li, H. SHAFTS: a hybrid approach for 3D molecular similarity calculation. 1. Method and assessment of virtual screening. J. Chem. Inf. Model. 51, 2372\u20132385 (2011).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Irwin, J. J. et al. ZINC20\u2014a free ultralarge-scale chemical database for ligand discovery. J. Chem. Inf. Model. 60, 6065\u20136073 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kim, S. et al. PubChem 2025 update. Nucleic Acids Res. 53, D1516\u2013D1525 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Decout, A., Katz, J. D., Venkatraman, S. & Ablasser, A. The cGAS\u2013STING pathway as a therapeutic target in inflammatory diseases. Nat. Rev. Immunol. 21, 548\u2013569 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Feng, Z. et al. Targeting colorectal cancer with small-molecule inhibitors of ALDH1B1. Nat. Chem. Biol. 18, 1065\u20131075 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, H. et al. SDEGen: learning to evolve molecular conformations from thermodynamic noise for conformation generation. Chem. Sci. 14, 1557\u20131568 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hoogeboom, E., Satorras, V. G., Vignac, C. & Welling, M. Equivariant diffusion for molecule generation in 3D. In Proc. 39th International Conference on Machine Learning (eds Chaudhuri, K. et al.) Vol 162, 8867\u20138887 (PMLR, 2022).<\/p>\n

Wang, Y. et al. A workflow to create a high-quality protein\u2013ligand binding dataset for training, validation, and prediction tasks. Digit. Discov. 4, 1209\u20131220 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

The UniProt Consortium et al. UniProt: the Universal Protein Knowledgebase in 2025. Nucleic Acids Res. 53, D609\u2013D617 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Jiang, Z., Xu, J., Yan, A. & Wang, L. A comprehensive comparative assessment of 3D molecular similarity tools in ligand-based virtual screening. Brief. Bioinform. 22, bbab231 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

W\u00f3jcikowski, M., Zielenkiewicz, P. & Siedlecki, P. Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field. J. Cheminform. 7, 26 (2015).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Fan, Z., Yang, Y., Xu, M. & Chen, H. EC-Conf: a ultra-fast diffusion model for molecular conformation generation with equivariant consistency. J. Cheminform. 16, 107 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Xu, M., Luo, S., Bengio, Y., Peng, J. & Tang, J. Learning neural generative dynamics for molecular conformation generation. In Proc. Ninth International Conference on Learning Representations (ICLR, 2021).<\/p>\n

Xu, M. et al. An end-to-end framework for molecular conformation generation via bilevel programming. In Proc. 38th International Conference on Machine Learning (eds Meila, M. et al.) Vol 139, 11537\u201311547 (PMLR, 2021).<\/p>\n

Shi, C., Luo, S., Xu, M. & Tang, J. Learning gradient fields for molecular conformation generation. In Proc. 38th International Conference on Machine Learning (eds Meila, M. et al.) Vol 139, 9558\u20139568 (PMLR, 2021).<\/p>\n

Wang, L. et al. Regularized molecular conformation fields. Adv. Neural Inf. Process. Syst. 35, 18929\u201318941 (2022).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Luo, S., Shi, C., Xu, M. & Tang, J. Predicting molecular conformation via dynamic graph score matching. Adv. Neural Inf. Process. Syst. 34, 19784\u201319795 (2021).<\/p>\n

Vieira Wyzykowski, A. B., Niazi, F. F. & Dickson, A. AGDIFF: attention-enhanced diffusion for molecular geometry prediction. J. Chem. Inf. Model. 65, 1798\u20131811 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lee, D., Lee, D., Bang, D. & Kim, S. DiSCO: diffusion Schr\u00f6dinger bridge for molecular conformer optimization. In Proc. AAAI Conference on Artificial Intelligence Vol 38, 13365\u201313373 (AAAI Press, 2024).<\/p>\n

Xu, M., Powers, A. S., Dror, R. O., Ermon, S. & Leskovec, J. Geometric latent diffusion models for 3D molecule generation. In Proc. 40th International Conference on Machine Learning (eds Krause, A. et al.) Vol 202, 38592\u201338610 (PMLR, 2023).<\/p>\n

Feng, S. et al. UniGEM: a unified approach to generation and property prediction for molecules. In Proc. Thirteenth International Conference on Learning Representations (ICLR, 2025).<\/p>\n

Vainio, M. J., Puranen, J. S. & Johnson, M. S. ShaEP: molecular overlay based on shape and electrostatic potential. J. Chem. Inf. Model. 49, 492\u2013502 (2009).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sastry, G. M., Dixon, S. L. & Sherman, W. Rapid shape-based ligand alignment and virtual screening method based on atom\/feature-pair similarities and volume overlap scoring. J. Chem. Inf. Model. 51, 2455\u20132466 (2011).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Xiong, J. & Oopstom. jiachengxiong\/ConfSeq: 1.1. Zenodo https:\/\/doi.org\/10.5281\/zenodo.19706011<\/a> (2026).<\/p>\n","protected":false},"excerpt":{"rendered":"Xu, Y. et al. Artificial intelligence: a powerful paradigm for scientific research. Innovation 2, 100179 (2021). Google Scholar\u00a0…\n","protected":false},"author":2,"featured_media":70284,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[24,25,39002,1632,372,617],"class_list":["post-70283","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai","tag-ai","tag-artificial-intelligence","tag-cheminformatics","tag-computational-science","tag-engineering","tag-general"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/70283","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=70283"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/70283\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/70284"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=70283"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=70283"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=70283"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}