Biomolecular Foundation Models for Discovery in Life Science

Protein foundation models are doing for proteins what GPT-4 did for language, learning the rules of folding, function, and evolution in a single, reusable neural network.

Protein foundation models—billions-parameter transformers like AlphaFold 3, ESM-3, Proteína, and Pallatom—collapse separate pipelines for fold prediction, mutational scanning, docking, and de-novo design into one promptable engine. Driven by scale (massive data/params), multimodality (joint sequence-structure-ligand embeddings), and controllability (prompting or quick fine-tunes), they have the potential to turn weeks of labwork or code into minutes of inference, reshaping protein R&D into a software-first workflow.

Next-gen foundation models (AlphaFold 3, ESM-3, Proteína, Pallatom) unify fold prediction, variant scoring, molecular docking, and on-demand protein design in one AI pipeline.

Soon, these models will move beyond folding to full-scale fabrication—designing multi-chain complexes, metabolic pathways, and even adaptive biomaterials on demand. Expect three currents to drive that future: continued scaling toward trillion-token training sets that capture rare folds; deeper cross-modal fusion that knits together cryo-EM maps, single-cell readouts, and reaction kinetics; and plug-and-play adapters (action layers) that translate a model’s coordinates directly into DNA constructs or cell-free expression recipes. Realizing this vision will require shared, high-quality structural and functional datasets, open benchmarking suites for generative accuracy and safety, and compute-efficient methods so labs and startups—not just hyperscalers—can iterate at foundation-model speed.

Genomic foundation models like Evo 2, Nucleotide Transformer, Enformer, and Geneformer are progressing from papers to early-stage products. 

These models are already topping benchmarks for variant effect prediction and single-cell annotation, but they still cover only a slice of genome biology today. Their recipe for progress so far is simple but powerful: massive scale (billions of DNA tokens + transformer parameters), self-supervised transfer (pretraining on omics data, then light fine-tuning), and for some models, multimodality (merging sequence, chromatin, and single-cell readouts in one model). As open datasets grow and GPU-efficient training improves, expect these “genomic foundation models” to become a standard layer in every life-science tech stack.

Genomic foundation models (Evo 2, Nucleotide Transformer, Enformer v2, scGPT) turn billions of DNA tokens into real-time variant effect prediction, single-cell annotation, and CRISPR-ready design, paving the way for genome-scale AI co-pilots and next-gen therapeutic discovery.

Next comes the era of genome-scale AI co-pilots: Studies like Geneformer and Evo 2 show evidence that transformer models can not only predict but also design useful CRISPR edits, de-novo promoters, and regulatory circuits entirely in silico. Emerging architectures like HyenaDNA, GenSLM, and Longformer-DNA can extend context windows beyond 1 Mbp, capturing 3D chromatin loops and long-range gene regulation. Eventually, multi-omic data can incorporate methylation, ATAC-seq, and spatial RNA onto sequence embeddings for richer biological insight. These advances will power real-time clinical variant triage, high-throughput enhancer discovery, and one-day, new therapeutic design approaches like programmable cell therapy, all from a single “genomic foundation model” API. Delivering that future demands open, privacy-safe genome datasets, standardized zero-shot benchmarks, and next-generation compute infrastructure and software that make trillion-token pretraining affordable outside hyperscale labs.

Chemical foundation models have shifted from research demos to real-world tools for drug discovery. 

Models like MoLFormer-XL, Uni-Mol 2, MolMIM, and GenMol analyze hundreds of millions of small-molecule strings (SMILES), 3D structures, and quantum-chemistry data to suggest new drug candidates, predict key biochemical properties in seconds, and outline possible synthesis routes. Three forces drive this progress: 3D-aware transformers and diffusion models that understand molecular shape; multi-task pretraining that lets one model handle property prediction, binding scoring, and synthesis planning; and simulation-augmented learning that embeds physics from quantum and molecular-dynamics simulations.

Small-molecule foundation models like MoLFormer-XL, Uni-Mol 2, MolMIM, and GenMol use SMILES strings, 3D structures, and quantum-chemistry data to generate drug candidates, predict ADMET properties, and plan synthesis routes via 3D-aware, multi-task, simulation-augmented transformers.

Large graph transformers trained on chemical reactions, molecular simulations, and 3D structures can propose syntheses, flag toxicity, and recommend green catalysts from one shared embedding. Their further development rests on three forces: ever-larger data/parameter scales, multimodal pretraining that fuses spectra and crystal structures with reaction conditions, and plug-in adapters that retarget a model to niche scaffolds in minutes. Broad deployment still needs open, high-quality reaction/property sets, rigorous benchmarks, and more efficient GPU throughput for billion-token runs; once in place, chemistry foundation models will cut lead-optimization time, curb lab waste, and make predictive synthesis routine in medicinal-chemistry workflows.