All Competitors

Autoregressive generative model for protein molecular dynamics that emits flexible-length trajectories frame by frame with anti-drifting sampling.

Protein language models trained on billions of natural and synthetic sequences for de novo design and zero-shot mutation-effect prediction.

4-bit QLoRA fine-tunes of ESM-2 for per-residue protein binding site prediction, released as a checkpoint family spanning 8M to 650M parameters.

Full-atom SE(3)-equivariant diffusion model that inpaints binding interfaces to design proteins that bind DNA, RNA, and small molecules.

Multimodal deep learning model that predicts protein-mediated chromatin contact maps and loops de novo from protein-binding profiles and sequence.

Enzyme backbone design model that adds substrate and catalytic-site control to a pretrained SE(3) flow-matching generator via a lightweight adapter.

PROTAC degrader generation pipeline that screens target-binding fragments, then builds molecules under structure and physicochemical constraints.

Protein language model for post-translational modifications, predicting PTM sites, types, and crosstalk across 40 modification classes.

Antibody CDR sequence and structure co-design from the whole antigen, using a relation-aware equivariant graph network with no specified epitope.

All-atom generative model for protein complexes that designs multi-chain binders from scratch and performs multimer folding and inverse folding.

Protein model quality assessment predicting per-residue lDDT for monomer and multimer interface models from graph-coupled ESM embeddings.

Protein-ligand binding affinity and mutation ΔΔG predictor fusing residue, ligand, and interaction graphs, evaluated on leak-proof LP-PDBBind splits.

Protein function annotation model that parses sequences into residue clusters via community detection on ESM-2 attention, then maps them to GO terms.

Bacterial proteome foundation model that learns contextualized gene and whole-genome representations from tens of thousands of complete genomes.

RNA language model transfer-trained from ESM-2 via a pseudo-protein alphabet mapping, outperforming 12 RNA language models in zero-shot evaluation.

Protein-DNA binding prediction and binder design from sequence, aligning protein and DNA language model embeddings instead of co-folding a complex.

Protein-ligand affinity foundation model that embeds pockets and ligands in one space, unifying virtual screening with hit-to-lead optimization.

Transformer-based generative language model for de novo RNA design, pretrained on 16 million non-coding RNA sequences from RNAcentral.

Per-residue pKa prediction from sequence alone, a thin MLP head on frozen ESM-2 embeddings reaching 0.48 RMSE across six titratable residue types.

Antibody CDR sequence-structure co-design by flow matching, starting from an informative structural prior rather than from Gaussian noise.

De novo protein binder design that recasts structure-predictor confidence as an energy function, replacing ipTM as the hallucination objective.

Molecular docking model that predicts protein-ligand binding poses with multi-stage Riemannian flow matching, yielding physically valid geometry.

De novo cyclic peptide binder design against a protein target, chaining a cyclized diffusion sampler, sequence design and structure prediction.