All Competitors

Multimodal model that designs small molecules from transcriptomic and cell-imaging perturbation phenotypes with a rectified flow transformer.

Enzyme design model that jointly generates enzyme sequences and substrate-binding pockets, conditioned on functional priors and substrate structure.

Structure-free protein-ligand binding affinity predictor built on OpenFold3 that scores potency from a protein sequence and a ligand SMILES string.

Contrastive geometric model unifying structure- and ligand-based drug design for zero-shot virtual screening, target fishing, and pocket selection.

Sequence-only latent diffusion model that designs target-specific peptide binders, cascaded with an affinity classifier through joint optimization.

Flow-matching model for therapeutic peptide design that co-designs sequence, structure, and molecular surface to disrupt protein-protein interactions.

Transformer foundation model pretrained on a biomedical knowledge graph for zero-shot drug repurposing, target, and adverse-effect prediction.

Generative language model for phenotype-driven drug discovery, proposing small-molecule structures from up- and down-regulated gene signatures.

Peptide developability predictor scoring solubility, permeability, toxicity, and binding from amino-acid sequences or chemically modified SMILES.

Peptide language model trained on HELM notation, a DeBERTa encoder for property prediction on macrocyclic and non-canonical medium-sized peptides.

Graph transformer foundation model for glycans, learning reusable embeddings of branched carbohydrate structures for glycomics prediction tasks.

Designs synthesizable PROTAC degraders from reaction templates and purchasable building blocks, with reinforcement learning tuning the generator.

Structure-based drug design model that unifies de novo generation, docking, conformer generation, and pharmacophore conditioning via flow matching.

All-atom generative model for de novo protein and peptide binder design against diverse biomolecular targets, wet-lab validated across 26 targets.

Flow-matching model that jointly samples 3D de novo molecules and several low-energy conformers, extending to pocket-conditioned ligand design.

Diffusion model for structure-based drug design that jointly generates 3D ligands and holo pocket conformations from an apo protein structure.

Pretrained language model for 3D molecule generation in protein pockets, unifying de novo and fragment-based drug design in one multi-task framework.

Multimodal conversational LLM for metabolite analysis, fusing a molecular-graph GNN and molecular-image CNN with a Vicuna-13B language backbone.

Equivariant diffusion model that converts peptide binders into drug-like small molecules, generating peptidomimetics inside the target protein pocket.

Multi-target drug discovery framework pairing a diffusion-transformer generator with evolutionary latent-space search and synthesis-aware scoring.

Repurposes a pretrained large language model into an encoder for MS/MS spectra, embedding them for compound identification by spectral library search.

Structure-based drug design model that generates ligands for a protein pocket, pairing a diffusion structure encoder with preference optimization.

Foundation model for tandem mass spectrometry that embeds MS/MS spectra into a learned chemical space, resolving isomers and classifying disease.

Open-source Apache-2.0 reproduction of AlphaFold3 that predicts all-atom structures of proteins, RNA, DNA, small molecules, and their complexes.