DeepMind is building a single system meant to read regulatory DNA as a unified code. IBM’s approach centers on decomposing biological questions into well-defined tasks, with models optimized for the mathematical and biological structure of each domain.
“Our work on Biomedical Foundation Models (BMFM) takes a more practical, modular approach,” said Michal Rosen-Zvi, Director of AI for Healthcare and Life Sciences at IBM Research, in an interview with IBM Think. “We decompose complex biological questions into well-defined components and identify the mathematical and algorithmic innovations required for the specific tasks at hand.”
Based on this analysis, IBM develops specialized models tailored to distinct domains, including RNA transcriptomics, DNA sequence analysis, and small-molecule and protein representation, according to Rosen-Zvi. “Each model is designed to optimally capture the modalities most relevant to its domain, whether that is primary sequence, two-dimensional structure, three-dimensional conformation or, in the case of our RNA models, mathematical representations that more faithfully capture whole‑genome expression at the cellular level,” she said.
Rosen-Zvi said IBM’s DNA work tries to avoid treating the genome as a single “standard” sequence. “Importantly, in our DNA models we explicitly incorporate population-level variation, training not only on reference sequences but also on SNPs and other mutable sites,” she said. That design, Rosen-Zvi explained, lets the models learn evolutionary and functional signals that a single reference genome can’t capture—signals that might otherwise require training on many thousands of whole genomes to approximate.
Rosen-Zvi framed biomedical foundation models as tools that are both powerful and workable in practice. “Overall, the BMFM approach emphasizes efficient training and inference and is particularly well suited to problems where the underlying biology spans multiple layers of information, abstraction and observation,” she said. In her view, that’s exactly the terrain scientists have to cross when they try to explain disease, pinpoint drug targets, propose mechanisms of action, generate candidate compounds and predict which ones are worth pursuing.
IBM has been focusing its recent modeling work on two areas of drug development that tend to consume time and money: biologics and small molecules. She pointed to IBM’s MAMMAL, which is designed to predict antibody-antigen binding strength. She also highlighted IBM’s MMELON, which she said has performed well at predicting the therapeutic properties of small-molecule candidates, an early readout that can help teams decide what’s worth pursuing before lab work begins.
A new IBM paper, co-authored with the Cleveland Clinic, offers a clearer look at how MMELON works. It describes a “multi-view” method for representing molecules, which IBM Research has presented in the paper as a case for domain-specific foundation models in biomedicine. The project grew out of IBM’s Discovery Accelerator Partnership with the Cleveland Clinic, a collaboration the two organizations have described as using AI and quantum computing to speed biomedical discovery.
IBM Research is also plugged into a much bigger data-building effort. It recently joined LIGAND-AI, a consortium announced in January 2026 that aims to generate open, high-quality datasets of protein-ligand interactions. The project announcement said the consortium, led by Pfizer and the Structural Genomics Consortium, includes 18 partners across nine countries.
Organizers said the initiative has a budget of more than 60 million euros and will probe thousands of proteins tied to both existing treatments and major unmet needs, including rare diseases, neurological conditions and cancer. The Structural Genomics Consortium said the project plans to generate billions of data points using complementary screening technologies, creating a resource that researchers worldwide can use to train and benchmark AI systems that predict molecular interactions.
The market for AI in biotechnology is expanding rapidly. Precedence Research projects continued double-digit growth globally, with estimates pointing to a market exceeding USD 25 billion by the mid-2030s, according to a January 2026 analysis by Ardigen. The US market alone was approximately USD 2.1 billion in 2025, with growth driven primarily by adoption in drug discovery, genomics and precision medicine, the analysis stated.