Bio Tools

Standardize molecules and turn atom-mapped reactions into explicit bond edits.

Structure prediction for proteins, DNA/RNA, ligands, and ions. Supports co-folding.

Biomolecular interaction prediction, and protein structure prediction.

Molecular structure prediction, including proteins. Similar to AlphaFold3.

Structure prediction for proteins, DNA/RNA, ligands, and ions.

Protein structure prediction; fast.

Deep-Learning models for predicting the structures of immune proteins.

Predict the structure of a cyclic peptide or a cyclic-peptide complex.

Designs proteins and peptides that bind to a wide range of biomolecular targets.

Design de novo protein or peptide binders against a target structure.

A free and open-source software suite for high-performance molecular dynamics and output analysis.

Identify germline V(D)J matches in an antibody sequence.

Humanize antibody sequences or estimate their humanness.

Structure-based antibody design using inverse folding

Design antibody sequences from a backbone structure.

ProteinMPNN

Protein sequence prediction, to conform with backbone coordinates. Does not take external molecules into account. A useful step after RFDiffusion in a protein design pipeline, and before validation with structure prediction.

LigandMPNN

Protein sequence prediction, to conform with backbone coordinates. Takes external molecules into account; to some degree a superset of ProteinMPNN, but is a different model. A useful step after RFDiffusion in a protein design pipeline, and before validation with structure prediction.

Estimate changes in protein stability upon point mutation

RFdiffusion3

Generates protein backbone coordinates around proteins, small molecules, nucleic acids, and metals. Given geometric and other constraints, specifies backbone geometry. A useful first step in a protein design pipeline.

Design antibody or nanobody binders against a target structure.

Efficient generation of epitope-targeted de novo antibodies

A protein design framework for antibody binder design

Design antibody CDRs against a target antigen by inverse folding.

A graph neural network (GNN) trained using transfer learning to predict changes in stability for protein point mutants

Predict Tier-1 ADME summary properties (lipophilicity, permeability, and solubility) for a batch of small molecules by SMILES.

Fast protein design through all-atom SE(3)-equivariance

Predict 30 spatial developability descriptors for an antibody from sequence alone.

Score how likely a peptide-MHC class I pair is to provoke a CD8 T-cell response.

Score how likely a peptide-MHC class II pair is to provoke a CD4 T-cell response.

Predict whether a protein will be soluble and usable when expressed in E. coli.

Predict the melting temperature of a protein from its sequence.

Map aggregation-prone patches onto a protein structure and score them.

Predict an enzyme turnover number from its sequence and a substrate structure.

Predict kcat, Km, or Ki for an enzyme and substrate, with an uncertainty estimate.

Number an antibody or TCR sequence and report its regions and liabilities.

Generate an ensemble of protein-ligand poses and side-chain conformations. Given a protein pocket and ligand, estimates how the ligand and sidechains will arrange themselves.