All Competitors

Sequence-to-ensemble predictor that generates conformational ensembles of intrinsically disordered proteins zero-shot, with no per-sequence refitting.

Geometric deep learning model that learns atomic-scale representations of molecular interfaces across proteins, small molecules, and nucleic acids.

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

Unified drug design engine for protein-ligand structure prediction, binding affinity estimation, and compound generation from Isomorphic Labs.

Protein structure tokenizer that encodes a whole structure globally, with each successive token adding detail for adaptive-length representations.

Protein structural homology search from sequence alone, embedding proteins so that structural similarity becomes a fast nearest-neighbor lookup.

LoRA adapter on ProstT5 predicting per-residue distributions over Foldseek 3Di tokens, capturing conformational flexibility from MD trajectories.

Multimodal protein language model that adds a continuous-token diffusion head to a discrete pLM, modeling structure without vector quantization.

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

Energy-based model of protein conformational space, turning a diffusion model into a statistical potential for structure ranking and mutation scoring.

RNA inverse-folding model that generates sequences predicted to fold into a target 3D backbone, capturing non-canonical pairs and tertiary motifs.

Protein-ligand cofolding model that predicts 3D complex structures with SO(3)-equivariant diffusion, trained on physics-based synthetic data.

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

Structure prediction backbone that swaps AlphaFold3-style triangle attention for triangle multiplication, cutting compute without losing accuracy.

Flow-matching model that predicts protein conformational ensembles across the order-disorder continuum, from folded domains to disordered chains.

Protein conformational sampling framework that steers a retrained OpenFold with diverse secondary-structure predictions to recover alternative states.

Protein foundation model with 3B parameters, pretrained jointly on sequence and 3D structure via masked language modeling and diffusion denoising.

GPCR structure prediction and peptide design model that generates linear and cyclic peptide agonists carrying noncanonical amino acids, zero-shot.

RNA foundation model pretrained jointly on sequences and secondary structures for structure prediction, homology and splice site classification.

Single-sequence protein structure predictor that adapts image diffusion to generate 2D inter-residue templates, folding proteins without an MSA.

Open model that jointly predicts biomolecular structure and small-molecule binding affinity, approaching FEP+ accuracy in seconds on a single GPU.

De novo protein design from natural language: a 16B-parameter framework turning text descriptions into sequences via structure-conditioned generation.

Open-source PyTorch reproduction of AlphaFold 3 under Apache 2.0, matching or exceeding AF3 on protein-ligand, protein-protein, and RNA benchmarks.

Variational autoencoder that learns interpretable representations of protein subtomograms from cryo-ET, trained on 5.8 million synthetic particles.