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Explore tools for protein design, structure prediction, molecular docking, molecular dynamics, and drug discovery. Search the catalog or filter by category, input type, application, and tag.
Design antibodies, nanobodies, scFvs, and peptides with high affinity and low immunogenicity.
Fold and score many binder candidates with a unified, machine-readable output.
Design next-generation enzymes by co-optimizing for catalytic activity, stability, and solubility.
Commercial friendly alternative to AlphaFold3 with competitive MSA-less option.
Open-source AlphaFold3-class model (Boltz-2) with built-in affinity prediction.
Open-source all-atom co-folding model for proteins, nucleic acids, ligands, and ions.
Another AlphaFold3 implementation developed by the ByteDance team.
Open-source AlphaFold3-class model (IntelliFold) with fast, accurate structure prediction across biomolecules.
Accurately predict protein and complex structures at the atomic level using their amino acid sequence.
Generate high-affinity binders for proteins, nucleic acids, and small molecules using an all-atom diffusion model.
Predict multiple protein developability properties from sequence in one batch workflow.
User-configured BAGEL workflows for mini-enzyme, mimic-enzyme, and binder design.
Fully atomistic binder and motif design with generative search and structure-prediction validation.
Score, rank, and compare protein structures with ProteinEBM energy.
Sample ProteinEBM folding dynamics and export trajectories.
Campaign-based protein binder design across multiple binder modalities.
One-shot design of functional protein binders.
PyRosetta-free one-shot design of functional protein binders.
Open-source AlphaFold3 reproduction achieving near-parity accuracy across biomolecular modalities.
Biohub all-atom ESMFold2 for proteins, nucleic acids, ligands, and complexes.
Interpret residue-level biological patterns learned by ESMC using sparse autoencoder features.
Design minibinders and antibody CDRs with ESMFold2 structural gradients and ESMC sequence regularization.
Zero-shot ESMC entropy and mutation scoring from sequence alone.
Enhanced molecular docking with deep learning.
Predict protein-ligand complexes using protein structure files and ligands in SMILES format.
Dock a ligand onto any protein receptor with high accuracy.
Generative antibody/nanobody design with fine-tuned RFdiffusion
All-atom generative diffusion model for designing proteins, nucleic acid binders, and enzymes with precise non-protein interaction conditioning.
Design functional enzymes from their reaction mechanisms using an atom-resolution generative model.
Design proteins, binders, and more with this protein diffusion model.
Run all-atom OpenMM molecular dynamics simulations with staged minimization, NVT, NPT, and production MD.
Run Martini coarse-grained OpenMM molecular dynamics simulations with staged minimization, NVT, NPT, and production MD.
Perform Molecular Dynamics using GROMACS framework, simulating many different solvent solute systems.
Calculate binding energetics for GROMACS trajectories using MMPBSA/MMGBSA calculations
Evaluate a fixed molecular geometry across a broad, provenance-rich catalog of electronic-structure methods and molecular potentials.
Simulate and map local binding-site hydration thermodynamics with a reproducible OpenMM and cpptraj GIST workflow.
BETA
Rank a positioned ligand series with OpenFE relative binding free energy calculations.
BETA
Compute an OpenFE absolute binding free energy from a positioned SDF ligand.
BETA
Compute OpenFE hydration free energies for small molecules.
Post-process molecular-dynamics trajectories into stability, flexibility, exposure, contact, and secondary-structure CSV outputs.
A deep learning framework for predicting enzyme kinetic parameters (kcat, Km, Ki).
Protein stability and binding energy analysis using EvoEF2.
An enhanced fork of AutoDock Vina offering customizable scoring functions, improved sampling, and better performance for molecular docking simulations.
Predict protein stability from structure using ESM-IF.
PRODIGY predicts binding affinity and dissociation constants for protein–protein complexes based on their 3D structures.
Converts PDB files to CIF / mmCIF files and vice versa
Predict alternative sequences for an input protein structure with high accuracy. Also supports SolubleMPNN.
An open-source version of AlphaFold3 developed by an MIT lab.
Predict ADMET properties swiftly and accurately using machine learning.
SaProt integrates sequence and structure information through a structure-aware vocabulary to predict protein properties accurately.
Rapidly generate diverse and quality MSAs with support for various pairing modes.
Predict alternative sequences for an input protein structure with high accuracy. Also supports ProteinMPNN and SolubleMPNN.
Fix common issues with PDB files such as missing atoms.
Accurately predict protein solubility and expression / usability from amino acid sequence.
A structure-aware deep learning model for high-accuracy protein-protein binding affinity prediction (Kd).
Open-source all-atom foundation model for structure prediction and generative design.
Predict Toxicity and Synthetic Accessibility from SMILES text or file inputs.
Accurately predict protein structures at the atomic level using its amino acid sequence.
Protein structure prediction that's faster than AlphaFold2 and just as accurate.
Predict Kcats of complexes using a protein sequence and compounds in SMILES format.
SPRINT is a fast, accurate, and scalable deep learning framework for virtual screening of thousands of molecules.
Protein folding model that supports proteins, nucleotides, ligands, metal ions, and other small molecules.
Relax a protein structure using an AMBER settling protocol.
TemStaPro predicts protein thermostability from sequence at a range of temperatures.
Structure-based aggregation profiling with Aggrescan3D
ThermoMPNN Predicts protein stability changes with precision and efficiency for mutation analysis and design.
Analyze the impact of mutations on protein stability using EvoEF2.
A sequence based method for optimizing protein solubility.
PocketFlow is a Deep Generative Model that generates ligands for target protein binding pockets.
FlowDock predicts protein-ligand structures and binding affinities using geometric flow matching, enabling multi-ligand docking and fast virtual drug screening.
A geometric deep learning model for predicting binding site probability from a structure.
DeepImmuno is a CNN-based model for peptide immunogenicity prediction with state-of-the-art accuracy across viral and cancer datasets.
EpHod is a semi-supervised language model that predicts optimal pH for enzymes from sequence alone.
Predict alternative sequences for an input protein structure with high accuracy.
Efficiently produce accurate 3D structural alignments across diverse macromolecular forms and configurations
Use AlphaFlow to generate protein structures that closely reflect experimental and physiological conditions.
Generate conformers for small molecules and ligands using RDkit.
Powerful molecular docking algorithm for proteins and nucleotides.
Accurately predict protein complexes with specialized restraints.
Accurately predict small-molecule-binding residues using AlphaFold2 pairwise representation.
Fast & accurate deep learning model for predicting binding ∆∆G using folding energy principles and a ProteinMPNN-based inverse folding framework.
Construct phylogenetic trees from protein structures using Foldseek.
Predict protein annotations from sequence using ProtNLM.
Design Antibodies, Nanobodies, and T-Cell Receptors using Immune Builder's state-of-the-art generative models.
Predict peptide toxicity from single protein sequences or in batch using an accelerated algorithm.
Predict kcat and Km class ranges from one enzyme sequence and a substrate panel.
Converts PDB files to SDF files and vice versa
An unsupervised deep learning model for rapid and accurate prediction of protein stability changes upon mutation, based on an improved ProteinMPNN methodology.
Accurate de novo protein structure prediction without reliance on MSAs.
Design Antibodies for a target Antigen using the Antigen structure. DiffAb leverages a probabalistic diffusion model.
PDB2PQR converts PDB files to PQR format, adding missing atoms and assigning charges for electrostatics calculations.
Predicts subcellular localization sites from protein sequence.
High-throughput descriptor engine for ML-ready molecular fingerprints.
Rapidly infer maximum-likelihood phylogenetic trees for large sequence datasets.
Screen and evaluate early-stage PPI-targeting compounds with a tailored drug-likeness index.
Generates improved cyclic protein structures using a modified AlphaFold network.
A deep learning-based tool for multispecies codon optimization.
AI-driven antibody humanization + humanness scoring from natural repertoire data.
A protein language model-based tool for efficient, task-agnostic design of high-functionality protein variants.
Create protein variants using nothing but the amino acid sequence.
A CNN-based tool for rapid, gene-specific humanization and classification of antibody heavy and light chains.
DeepEMhancer is a deep learning approach for automatic post-processing of cryo-EM maps, performing masking and sharpening in a single step to improve interpretability.
Predict potential post translational modification sites from sequence data.
A sequence based method for detecting signal peptides.
Pocket-conditioned docking for small molecules or peptides.
Rapidly generate high-quality multiple sequence alignments for protein sequences.
A nucleotide sequence based method for optimizing protein expression.
ANARCI provides standardized numbering and chain classification for antibody and TCR domains.
CryoAtom builds atomic models from cryo-EM maps using local attention and 3D rotary position embedding, improving model completeness and speed while lowering resolution requirements.
Detect and summarize molecular interactions across uploaded structures.
Enzbert predicts enzymatic classes of protein sequences in batch or individually.
Boltz-2-compatible low-memory fork with extra chunking and bfloat16 controls.
Efficiently annotate disordered protein regions with a compact language model.
CAR-Toner is an AI tool for rapid prediction of CAR-T tonic signaling by calculating Positively Charged Patch (PCP) scores.
Predict conformational substates using AlphaFold2 on multiple sequence alignments.
Generative AI for small molecule design and optimization.
Identify likely metal and water binding sites from a protein structure.
Convert a PDB structure into FASTA sequences for each valid protein chain.
Use the Foldseek easy-cluster algorthim to cluster structures using a representative structure.
ImaPEp predicts binding probabilities for antibody–antigen pairs by representing their binding interfaces as 2D images and leveraging convolutional neural networks.
ProSST predicts protein mutation effects and functions by integrating sequence and structural data via quantized tokens and disentangled attention.
Protein structure clustering and visualization tool using TM-align/US-align structural alignment and dimensionality reduction techniques (UMAP, t-SNE, PCA).
Pangolin is a deep learning model to predict splice site strength and the impact of genetic variants on RNA splicing in multiple tissues.
Predict alternative sequences for an input Antibodies, Nanobodies, and Antigen-Antibody structures with high accuracy.
DnaChisel edits DNA sequences to satisfy biological constraints and optimize properties like codon usage, motif distribution, and GC content.
Predict nanobody melting temperature directly from amino-acid sequence.
ParaSurf is a deep learning framework that predicts paratope binding sites by analyzing molecular surfaces of antibodies/nanobodies to identify antigen-binding regions across the entire Fab/Fv domain.
Fragment-based molecular generation and optimization tool.
Chai-1 fork with Feynman-Kac steering for molecular-glue ternary complexes.
BETA
Discover and rank cryptic protein pockets through conformational ensemble analysis.
Structure- and ensemble-conditioned protein sequence design, scoring, and sidechain packing.
Assess the quality of protein-protein docking models using the native and predicted structure.
Predict alternative sequences for an input protein structure with high accuracy.
Split one structure into separate chain-level PDB or mmCIF files.
Pocket-conditioned peptide generation and redesign.
Pocket-conditioned small-molecule generation and fragment expansion.
Sequence-only pI/pKa prediction (IPC 1/2).
CryoSAMU enhances intermediate-resolution cryo-EM maps using a structure-aware multimodal U-Net, integrating map features with protein language model embeddings for faster, high-quality results.
Predict high-concentration monoclonal antibody viscosity classes.
ANARCII is a language model–based tool for scalable, accurate numbering and classification of antibody and TCR repertoires.
Fast structure similarity search across AlphaFold DB.
Rapidly generate multiple sequence alignments for protein sequences.
Predict protein-protein interaction probability from paired structures or sequences.
Compare protein-ligand interaction fingerprints across compounds with ProLIF.
Joint protein sequence-structure co-design with ligand, RNA, and DNA conditioning in one diffusion workflow.
Cluster same length proteins using only their structures.
A deep learning tool that predicts the conformational flexibility of antibody and T cell receptor (TCR) CDR3 loops, classifying them as 'rigid' or 'flexible'.
StrucTFactor leverages 3D protein structures for precise transcription factor prediction, outperforming existing methods.
Generates RNA sequences with precise structural fidelity and functional diversity for targeted applications
Find structurally similar compounds in PubChem from SMILES, SDF, or CCD queries.
Evaluate protein structure quality with a superposition-free local distance difference score
Open-source Chemprop ADMET prediction for small-molecule screening.
Use ABACUS-R to design protein sequences for a given backbone structure using an encoder-decoder model.
Easily perform transcript quantification using an input fastq file.
Render animated GIF and MP4 files from multi-model PDB structures.
Optimize enzyme thermostability, pH stability, solubility, and reaction rate with high accuracy.
Rank antibody candidates with pretrained AlphaBind, optionally fine-tuned on target-specific measurements.
BETA
Compare two protein structures through their geometric and chemical surfaces.
Differential Expression Analysis pipeline configured for two-condition experiments.
Estimate passive membrane permeability and insertion energy from 3D molecular structure.
Generate bulk 3D MOL2 structures from SMILES.
Predict zinc-binding sites from an uploaded protein structure.
Predict Caco-2 and MDCK permeability and efflux from small-molecule structures.
Calculate common molecular properties for small molecules using RDKit.
Measure protein surface exposure by structure, chain, and residue.
Analyze IDR molecular grammar features and GIN clusters from human IDs or custom IDR sequences.
Predict the minimum-free-energy structure of two interacting RNA strands.
Estimate local RNA accessibility and base-pair probabilities along a sequence.
Predict protein surface hydration waters from uploaded PDB or mmCIF structures.
Score genomic variants and generate DNA with Evo2.
Split interleaved FASTQ into left/right FASTQ with validation.
Evolve SpCas9 PAM preference using evolutionary algorithms.
Align uploaded biomolecular structures to a shared reference while preserving complete complexes.
Compare the aliphatic index of protein sequences.
Predict antibody PSR and SEC developability from paired variable-domain sequences.
Interpretable R-group SAR modeling and analog prioritization for congeneric small-molecule series.
Align protein or nucleotide sequences to a profile HMM with HMMER.
Machine learning models to predict SpCas9 PAM preference from amino acid sequence.
BETA
Confidence-ranked ligand and protein side-chain ensembles.
Build a reusable protein profile from an existing MSA or generate an alignment from sequences or a query.
Align two reusable profiles using symmetric probability overlap rather than an HMM method. Scores are in bits and have no calibrated E-values.
Align and rank protein candidates against one reusable position-specific protein profile. Scores are log-odds in bits, with no calibrated E-values or PSI-BLAST equivalence.
Find and rank ungapped profile-length windows in longer protein sequences. Scores are log-odds in bits, with no calibrated E-values or PSI-BLAST equivalence.
Reconstruct all-atom protein structures from C-alpha or reduced protein PDB models.
Calculate all-against-all backbone RMSD values between reference and mobile structure groups.
Identify locally stable RNA secondary structures within a selected window.
Predict a consensus RNA secondary structure and free energy from a prealigned RNA multiple-sequence alignment.
Compare RNA secondary structures using tree, string, or base-pair distance measures.
Count RNA secondary structures across discrete energy bands.
Find optimal RNA-RNA hybridization duplexes and their binding energies.
Evaluate the free energy of a supplied RNA sequence and secondary structure.
Predict RNA minimum-free-energy structures and optional base-pair probabilities.
Design RNA sequences that fold toward a specified secondary structure.
Scan for favorable RNA-RNA interaction sites and duplex energies.
Draw a supplied RNA secondary structure as a radial, circular, or alternative layout.
Enumerate RNA secondary structures near the minimum free energy.
Calculate RNA-RNA interaction energies that include site accessibility.
Measure protein compactness for a structure or selected chains.
Generate customizable RNA sequences in FASTA format.
Convert DNA and RNA strands from text, FASTA, or FASTQ.
Convert bulk SMILES to standard InChI and InChIKey.
Calculate interface confidence metrics from a structure and PAE matrix.
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