All Competitors

A hierarchical three-stage cascade that generates chemically modified and macrocyclic peptides in HELM notation, supporting de novo design and constrained infilling.

A signed heterogeneous graph foundation model pretrained on the SIGMA-KG knowledge graph for zero-shot drug mode-of-action, clinical response, and drug-drug interaction prediction.

A virtual cell foundation model pretrained on 23M+ cells from 5,000 patient samples for drug target and biomarker discovery.

A motif-aware graph diffusion transformer for controllable molecular generation that transfers to unseen properties by learning only lightweight task embeddings with the generator frozen.

A multi-modal Transformer that fuses LLM gene embeddings with biological knowledge graphs to predict single-cell transcriptomic responses to genetic perturbations.

Meta-learning framework that transfers perturbation responses across cell lines, donors, and drugs from a few seed perturbations, using one-eighth the parameters of typical single-cell foundation models.

A direction-aware foundation model trained on ~23M bulk RNA-seq differential-expression profiles that simulates coordinated gene dynamics in viral infection.

SMILES-based chemical language models pretrained on 100M+ molecules to natively represent therapeutic peptide chemistry, including non-canonical residues.

OpenAI's first life-sciences frontier reasoning model, optimized for multi-step scientific workflows spanning protein engineering, genomics, drug-target discovery, and biochemistry reasoning.

A Llama 3 model fine-tuned on a ChEMBL corpus that designs molecular linkers from natural-language geometry and physicochemical prompts, without task-specific re-training.

A transformer that predicts protein-RNA binding affinity from Boltz-2 pre-structural embeddings via cross-modal attention, without requiring predicted structures.

A family of SE(3)-equivariant molecular foundation models pretrained on ~70M DFT conformers, reaching state of the art on TDC ADMET and MoleHB property-prediction benchmarks.

Multimodal molecular foundation model fusing SELFIES sequences, 2D graphs, text descriptions, and knowledge-graph embeddings via contrastive pretraining for property prediction.

All-atom generative foundation model trained on 5M+ biomolecular complexes that designs small molecules, peptides, and nanobodies against a target site from one checkpoint.

A DeepSeek-7B-based multi-task large reasoning model that applies chain-of-thought reasoning and reinforcement learning across ~10 molecular science task families.

A 2.6B-parameter Liquid Foundation Model fine-tuned via Insilico's MMAI Gym to handle ADMET, retrosynthesis, drug-target activity, and molecular optimization in one checkpoint.

Graph Transformer foundation model integrating 3 million protein pockets and 5 million molecules as E(3)-equivariant graphs for joint protein-ligand geometric representation learning.

A multimodal, domain-constrained foundation model that self-supervises on ~500K PubChem molecules to jointly predict nine thermophysical properties of small molecules.

A knowledge-graph foundation model that injects cell-type-specific genetic associations into a biomedical knowledge graph to improve drug indication prediction and repurposing.

A polarizable electrostatic machine-learning interatomic potential extending MACE with long-range induction, trained on 100M OMol25 DFT calculations.

A retrieval-augmented foundation model for matched molecular pair transformations that proposes controllable, medicinal-chemistry-style analog edits.

A physically grounded diffusion model that generates continuous-time, all-atom biomolecular dynamics trajectories at a fraction of the cost of molecular dynamics simulation.

Protein-ligand foundation model that maps coarse-grained structural representations directly to binding affinity, running ~26x faster than Boltz-2.

Isomorphic Labs' unified AI drug design engine that doubles AlphaFold 3 accuracy on protein-ligand structure prediction and approaches gold-standard FEP+ methods for binding affinity.