🧪 Best AI tools for Protein structure & design (2026)
Structure prediction, protein language models and generative protein/binder design.
32 tools · 25 open source · each entry links to its website, code and paper.
Sequence-only pLM ensemble predicting membrane-protein interaction interfaces; proteome-scale atlas covering 80k+ reviewed TM proteins.
Text-to-structure all-atom protein design: generate monomers and binders from natural-language prompts.
Google DeepMind diffusion pipeline for de novo enzyme design via catalytic motif scaffolding.
Open generative model for designing protein and peptide binders against diverse targets.
NVIDIA latent flow model for joint atomistic protein sequence-structure generation.
Atom-level enzyme active-site scaffolding with diffusion, no sequence-position constraints.
NVIDIA flow-matching model for large-scale protein backbone generation.
Trainable PyTorch reproduction of AlphaFold 3 by ByteDance.
Generative model emulating protein equilibrium structural ensembles.
Open biomolecular interaction models (Boltz-1, Boltz-2) for complex structure and binding-affinity prediction.
One-shot de novo binder design pipeline with AF2 hallucination.
Multimodal foundation model for molecular structure prediction.
Multimodal generative protein language model over sequence, structure and function (ESM3; also ESM C embeddings).
Structure prediction of proteins, nucleic acids, ligands and their complexes; free web server and code, weights on request for non-commercial use.
Structure-based de novo antibody and nanobody design with fine-tuned RFdiffusion, ProteinMPNN and RF2.
Paired-chain antibody language model trained to reduce germline bias.
Generalized biomolecular modeling for proteins, nucleic acids, small molecules.
Deep learning structure prediction for antibodies, nanobodies and TCRs (ABodyBuilder2 etc.).
Fast antibody structure prediction using AntiBERTy embeddings and graph networks.
Diffusion model for de novo protein backbone design and binder design.
Protein language models and single-sequence structure prediction (Meta repo archived; models still widely used via Hugging Face).
Protein language models (up to 6.4B) for sequence generation and fitness prediction.
Deep-learning inverse folding: designs sequences for given backbones.
Fast and accurate protein structure search.
Antibody language model for heavy/light chains, used for residue restoration and embeddings.
BERT-style antibody language model trained on 558M OAS sequences.
Trainable open-source reproduction of AlphaFold2 in PyTorch.
Antibody humanization (Sapiens) and humanness evaluation (OASis) platform.
Fast, accessible protein folding with MMseqs2 + AlphaFold in Colab.
DeepMind's open-source protein structure prediction model (monomer and multimer).
200M+ predicted protein structures from DeepMind and EMBL-EBI.
No-code web platform running 100+ protein and molecular ML models.