All Competitors

Protein language models pretrained on Rosetta biophysics simulations rather than evolutionary data, then finetuned on small experimental assays.

Deep network that predicts structures of full biological assemblies: proteins, nucleic acids, small molecules, metals, and covalent modifications.

Antimicrobial peptide generator running denoising diffusion in the continuous ESM-2 embedding space, validated in mouse infection models.

Blind protein-ligand docking that transfers to binding domains absent from training, scoring 22.6% top-1 on DockGen and 50% on PoseBusters.

Protein large language model adapted from LLaMA-2 that unifies sequence generation and superfamily classification in one 7B-parameter framework.

All-atom 3D molecular foundation model pretrained across small molecules, proteins, and complexes with an E(3)-equivariant denoising objective.

Compound-protein interaction prediction coupling a chemical language model to a protein language model via a cross-attention block.

Protein-ligand binding affinity prediction from an amino acid sequence and a ligand SMILES string, with no structure, docked pose, or pocket needed.

Peptide tandem mass spectrum prediction across the full fragment ion series, with neutral losses and modification-specific peaks beyond b/y ions.

Protein conformational ensemble generator that fine-tunes AlphaFold 2 with flow matching, sampling protein dynamics beyond a single static structure.

Antibody language model trained on paired and unpaired OAS sequences to suggest non-germline mutations instead of reverting them to germline.

Protein homology search via contrastive per-residue ResNet embeddings, acting as a pre-filter at least 5x faster than the one inside HMMER3.

Multimodal protein pre-training framework jointly learning sequence, 3D structure, and surface representations via implicit neural representations.

Structure-based drug design diffusion model that re-extracts the essential binding subcomplex from a pocket at every step of 3D ligand generation.

RNA-binding protein affinity prediction at single-base resolution from sequence alone. One model spans 155 RBP targets across three cell lines.

Unified 100-billion-parameter protein language model combining autoencoding and autoregressive objectives for protein understanding and generation.

Cryo-electron tomography membrane analysis pipeline pairing generalizable U-Net membrane segmentation with mesh-based particle localization.

Protein function prediction models that assign Gene Ontology terms using language model embeddings and neuro-symbolic reasoning over GO axioms.

Peptide language model that generates antimicrobial, anticancer, and target-binding sequences, adapted per task by Mixture-of-Experts plugins.

Codon-level BERT model that captures genomic signals invisible to amino acid models, outperforming billion-parameter PLMs with just 86M parameters.

Inverse protein folding from backbone coordinates, chaining a pretrained structure encoder into a pretrained sequence autoencoder on small data.

Binding energy for protein-ligand, protein-protein, and antibody-antigen complexes is read off an energy model trained on crystal structures alone.

Controllable protein design by prefix-tuning a protein language model with learned virtual tokens that combine for multi-property generation.