Can you actually use it commercially?

Directory

• AlphaProteo

  De novo binder and protein design

  DeepMind's binder design system - no code or weights released; trusted-tester access only.

  What it doesDesigns de novo protein binders to a specified target site, with reported nanomolar-to-picomolar affinities straight out of the model. Input (per the trusted-tester notebook): a target PDB ID and the target region; output: binder designs returned by DeepMind, not computed locally. There is no model you can run.

  Google DeepMind·Hosted service·Weights not openly downloadable

  ?UnverifiedApache License 2.0 covers only the input-preparation notebooks in the public repo; the model itself is unreleased

  The only public artefact is the ap_trusted_testers repository, whose README describes a Colab that helps 'users of the AlphaProteo Trusted Tester Program' prototype requests and visualise target regions - an input builder, not the model. No weights, no inference code and no terms for commercial use of the designs are published, so rights are unverified.

  ?Cost unverifiedNo price exists because no product exists. Access is by DeepMind's trusted-tester programme, whose commercial terms are not published.

  Source 1 Source 2 checked 15 Oct 2026

• BindCraft

  De novo binder and protein design

  AlphaFold2-backpropagation pipeline that designs binders end to end.

  What it doesOne-command de novo binder design. Input: target PDB, chain and target residues; output: binder sequences plus predicted complex PDBs and filter statistics (iPTM, pAE interaction, ddG, clashes), using AlphaFold2 backpropagation and MPNN redesign.

  Martin Pacesa / Correia lab, EPFL·Single GPU·Open weights

  !RestrictedMIT License

  Code is MIT, but the README states the installer installs PyRosetta, 'which requires a license for commercial purposes'. PyRosetta's own page confirms licensing is free for academic/non-profit and fee-based for commercial users. AlphaFold2 parameters it uses are CC BY 4.0, which does allow commercial use.

  $Paid licenceThe pipeline itself is free, but its installer pulls in PyRosetta, and PyRosetta's licensing page states that commercial users pay a fee (academic and non-profit use is free).…

  Source 1 Source 2 +2 morechecked 15 Oct 2026

• BoltzGen

  De novo binder and protein design

  Generative model for binder design across protein and non-protein targets.

  What it doesGenerative binder design against protein and non-protein targets. Input: target (protein, nucleic acid or small molecule) plus binding-site spec in YAML; output: designed binder backbones and sequences with self-consistency/refolding metrics.

  Boltz team (MIT CSAIL and collaborators)·Heavy GPU·Open weights

  ?UnverifiedMIT License

  The repo LICENSE is MIT, covering the code. The README documents automatic download of ~6 GB Boltz weights plus ESMFold2 weights but makes no license statement for those weights, so weight terms are unverified.

  0Free commerciallyNo fee: code is MIT and the weights download automatically. The weights' own terms are not published, so 'free' here describes price, not a settled right to use them commercially.

  Source 1 Source 2 checked 15 Oct 2026

• Chroma

  De novo binder and protein design

  Programmable protein generator with composable conditioners - weights are academic-only.

  What it doesGenerates all-atom proteins and complexes under programmable constraints. Input: 'conditioners' you compose - symmetry, shape, substructure, natural-language-style class conditioning; output: structures plus sequences, since it models both. Also does sequence design on given backbones and side-chain packing.

  Generate Biomedicines·Single GPU·Open weights

  !RestrictedApache License 2.0 (code); Chroma Parameters License (weights, academic/non-profit only)

  The README's License section is explicit: code is Apache-2.0, but 'Chroma weights are freely available to academic researchers and non-profit entities' under the Chroma Parameters License, and anyone wanting to use the parameters 'beyond the scope of the rights granted in the License (including for commercial purposes)' is told to contact [email protected]. So commercial use needs a negotiated licence from the company.

  $Paid licenceFree for academics and non-profits; a company must contact Generate Biomedicines' licensing address for terms. No price is published, so assume a negotiated fee.

  Source 1 Source 2 checked 15 Oct 2026

• EvoBind

  De novo binder and protein design

  In-silico directed evolution of cyclic and linear peptide binders with AlphaFold.

  What it doesEvolves a peptide binder against a target without needing a known binding site. Input: target sequence/structure and optional target residues; output: optimised peptide (including macrocyclic) sequences with AlphaFold-predicted complexes, by iterative mutation under an AlphaFold-based objective.

  Patrick Bryant (Stockholm University / SciLifeLab)·Heavy GPU·Weights not openly downloadable

  ?Unverifiedunverified - no LICENSE file published in the repository

  Checked via the GitHub licence API and the repository root on the date shown: the repo publishes no LICENSE file and the README states no terms, so by default no rights are granted and the position is unverified. It also runs AlphaFold2 parameters (CC BY 4.0) underneath, which are themselves fine for commercial use.

  ?Cost unverifiedNo fee is charged for the code, but with no licence published there is no stated right to use it commercially at any price.

  Source checked 15 Oct 2026

• Genie 2

  De novo binder and protein design

  Diffusion model for unconditional backbone generation and multi-motif scaffolding.

  What it doesGenerates protein backbones, optionally scaffolding several functional motifs at once. Input: a length range, or one or more motif PDB fragments with their desired spacing; output: backbone PDBs, which you then sequence-design and refold to check designability. Checkpoints ship in the v1.0.0 release.

  AQ Laboratory (Mohammed AlQuraishi lab, Columbia University)·Single GPU·Open weights

  ✓Commercial OKApache License 2.0

  Apache-2.0 LICENSE in the repo. The README states the repository provides training and inference code 'as well as model weights', released from the same repository, and no separate weight terms are given - so the permissive grant is the only licence in play.

  0Free commerciallyFree: Apache-2.0 code and checkpoints downloaded from the GitHub release, no fee.

  Source 1 Source 2 checked 15 Oct 2026

• La-Proteina

  De novo binder and protein design

  Partially latent flow-matching model generating sequence and all-atom structure jointly.

  What it doesGenerates a protein's sequence and full all-atom structure (backbone plus side chains) in one pass, including atomistic motif scaffolding. Input: length, or a motif with its atom coordinates; output: all-atom designs that do not need a separate inverse-folding step to get a sequence.

  NVIDIA (BioNeMo)·Heavy GPU·Open weights

  ✓Commercial OKApache License 2.0 (code); NVIDIA Open Model License Agreement (weights); separate dataset licence

  This repo splits its licences into a LICENSE/ directory: license_code.txt is Apache-2.0, license_weights.txt is the NVIDIA Open Model License Agreement, and license_datasets.txt covers the data. The Open Model License is the permissive one NVIDIA uses to allow commercial use of model outputs and derivatives, in contrast to the plain 'NVIDIA License' on Proteina which is non-commercial - read the conditions (attribution, trustworthy-AI and no-reverse-engineering terms) before shipping.

  0Free commerciallyNo fee. Code Apache-2.0 and weights under the NVIDIA Open Model License, which grants use without payment subject to its conditions.

  Source 1 Source 2 +1 morechecked 15 Oct 2026

• PepMLM

  De novo binder and protein design

  Masked-language-model generation of linear peptide binders from target sequence alone.

  What it doesGenerates peptide binders without any structure. Input: the target protein sequence and the desired binder length; output: candidate peptide sequences, produced by masked-token generation conditioned on the target - cheap enough to run on a laptop-class GPU.

  Chatterjee Lab (Duke / Programmable Biology)·Single GPU·Open weights

  ×Non-commercialModel card declares MIT, but access is gated behind terms that require non-commercial use

  A genuine contradiction, read on the date shown: the Hugging Face card for ChatterjeeLab/PepMLM-650M declares license: mit, while the same card's gated-access fields make you tick 'I agree to use this model for non-commercial use ONLY', plus authorship, data-sharing and no-patent conditions. The GitHub repo publishes no LICENSE file at all. Those access terms are what you actually accept to obtain the weights, so treat it as non-commercial until the authors reconcile the two.

  ×No commercial optionNo money changes hands, but the access agreement demands non-commercial use and rights concessions, so there is no commercial path. No commercial licence is offered.

  Source 1 Source 2 checked 15 Oct 2026

• Proteina

  De novo binder and protein design

  Large flow-matching backbone generator from NVIDIA - research and evaluation only.

  What it doesGenerates protein backbones at scale, including long chains and fold-class-conditioned samples. Input: length, fold class label or motif; output: backbone structures for downstream sequence design. Trained on a very large synthetic+PDB corpus.

  NVIDIA (BioNeMo)·Heavy GPU·Open weights

  ×Non-commercialNVIDIA License (non-commercial)

  The LICENSE file's section 3.3 'Use Limitation' states the work and any derivative works 'only may be used or intended for use non-commercially', defining non-commercially as 'for research or evaluation purposes only', with an explicit carve-out letting NVIDIA itself use it commercially. Unambiguous: a company cannot use this in product work.

  ×No commercial optionNo commercial option exists at any price: the licence limits use to research or evaluation and NVIDIA publishes no commercial licence for it.

  Source checked 15 Oct 2026

• RFdiffusion

  De novo binder and protein design

  Diffusion model that generates protein backbones, including binders to a target.

  What it doesGenerates protein backbones. Input: a target structure (PDB) with hotspot residues, or just a length/symmetry spec; output: backbone-only PDBs (no sequence) for de novo binders, scaffolds or motif grafts. You then run inverse folding (ProteinMPNN) and a structure predictor to filter.

  Institute for Protein Design, University of Washington (RosettaCommons)·Single GPU·Open weights

  ✓Commercial OKBSD License (as titled in the repo LICENSE file)

  The LICENSE text states it covers both source code and the model weights referenced in the README; it is a permissive BSD-style license with no field-of-use restriction.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source 1 Source 2 checked 15 Oct 2026

• RFdiffusion2

  De novo binder and protein design

  Atom-level successor to RFdiffusion, aimed at enzyme and motif scaffolding.

  What it doesGenerates backbones around an atomic motif without having to specify its indices. Input: a set of catalytic/functional atoms or a target site; output: backbone PDBs scaffolding that geometry, which you then sequence-design (LigandMPNN) and refold. Inference only in this release.

  Institute for Protein Design, University of Washington (RosettaCommons)·Heavy GPU·Open weights

  ?UnverifiedBSD 3-Clause License (code); no licence stated for the downloaded weights

  The repo LICENSE.md is BSD 3-Clause (2025, Institute for Protein Design) and mentions only source and binary forms; the README's setup step downloads large model weight files and containers with no licence statement of their own, so the weights' commercial terms are unverified. The README also marks the release 'under construction'.

  ?Cost unverifiedNothing is charged for the download. Whether a company may use the weights at all is unsettled, so no price can be stated.

  Source 1 Source 2 checked 15 Oct 2026

• ABodyBuilder3

  Antibody-specific design

  Updated antibody structure predictor with per-residue error estimates.

  What it doesUpdated antibody Fv predictor. Input: heavy and light chain sequences; output: an Fv structure plus a per-residue predicted error (RMSD) estimate, so you can tell which CDR loops to trust before docking.

  Exscientia·Single GPU·Weights: unverified

  ✓Commercial OKApache License 2.0

  LICENSE.txt in the repo is Apache-2.0; permissive, commercial use allowed.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source 1 Source 2 checked 15 Oct 2026

• ANARCI

  Antibody-specific design

  Numbers antibody and TCR sequences (IMGT, Kabat, Chothia).

  What it doesAntibody numbering. Input: antibody or TCR sequences (FASTA); output: the same sequences numbered in IMGT/Kabat/Chothia/Martin with germline V/J assignment and CDR boundaries - the preprocessing step almost every antibody tool expects.

  OPIG, University of Oxford·CPU·Open weights

  ✓Commercial OKBSD 3-Clause style license (LICENCE file, 2019 Deane et al.)

  The LICENCE file contains BSD 3-Clause redistribution terms with no field-of-use restriction; GitHub's license detection also reports BSD-3-Clause.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source 1 Source 2 checked 15 Oct 2026

• ANARCII

  Antibody-specific design

  Language-model successor to ANARCI for immune receptor numbering.

  What it doesLanguage-model antibody/TCR numbering. Input: immune receptor sequences; output: numbered sequences with CDR annotation, handling unusual, truncated and non-human sequences where HMM-based ANARCI fails.

  OPIG, University of Oxford·CPU·Open weights

  ✓Commercial OKBSD 3-Clause License

  LICENCE file reported by the GitHub license API as BSD-3-Clause; permissive.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source 1 Source 2 checked 15 Oct 2026

• Chai-2

  Antibody-specific design

  Chai Discovery's zero-shot antibody and miniprotein design model - access by partnership only.

  What it doesDesigns de novo antibodies, nanobodies and miniproteins against a target epitope, reported to need only ~20 designs per target. Input (as described by the vendor): a target antigen and epitope; output: scFv/VHH/miniprotein designs. There is no public code, no downloadable weights and no self-serve API.

  Chai Discovery·Hosted service·Weights not openly downloadable

  ?Unverifiedunverified (no public licence; access granted case-by-case under the vendor's Responsible Deployment policy)

  No licence text exists to read. The announcement says access is being opened to 'a number of academic and industry partners' via an early-access sign-up and granted under a Responsible Deployment policy. Terms for a commercial partner are not published, so rights are unverified. Listed here because it is widely cited and people ask - not because it is usable off the shelf.

  ?Cost unverifiedNo price is published. Access is by application, and nothing in the announcement states whether partners pay, grant rights, or share results.

  Source checked 15 Oct 2026

• DiffAb

  Antibody-specific design

  Diffusion model for antigen-specific CDR design and optimisation.

  What it doesDesigns or redesigns CDRs against a given antigen. Input: an antibody-antigen complex structure (or a docked framework) and the CDR to vary; output: sampled CDR sequence-structure pairs, supporting de novo design, sequence-structure co-design and optimisation of an existing antibody.

  Shitong Luo et al. (academic release)·Single GPU·Open weights

  ✓Commercial OKApache License 2.0

  Apache-2.0 LICENSE in the repo; the trained checkpoints are distributed from the same repository with no separate terms. It does depend on antibody structure tooling, which you should licence-check separately.

  0Free commerciallyFree: Apache-2.0 code and checkpoints, no fee.

  Source checked 15 Oct 2026

• Germinal

  Antibody-specific design

  End-to-end pipeline for de novo nanobody and scFv design.

  What it doesEnd-to-end de novo nanobody and scFv design. Input: target PDB + epitope residues; output: ranked designed antibody sequences with predicted complexes, by combining backbone hallucination, sequence design and in-silico filtering in one pipeline.

  Santiago Mille-Fragoso et al. (open-source release)·Heavy GPU·Weights: unverified

  ✓Commercial OKApache License 2.0

  Apache-2.0 LICENSE file in the repo; permissive, commercial use allowed. Any bundled third-party weights/dependencies should be checked separately.

  0Free commerciallyApache-2.0 code, no fee. Check the dependency stack it installs before commercial deployment - the pipeline pulls in several third-party models.

  Source checked 15 Oct 2026

• IgFold

  Antibody-specific design

  Fast antibody Fv structure prediction from sequence.

  What it doesAntibody Fv structure prediction. Input: heavy and light chain sequences; output: an Fv PDB with per-residue error estimates, in under a minute - fast enough for whole design libraries, but the licence is academic-only.

  Gray lab, Johns Hopkins University·Single GPU·Open weights

  ×Non-commercialJHU Academic Software License Agreement

  LICENSE.md states only a non-exclusive, nontransferable license for non-commercial purposes; 'Commercial use of the Software requires a separately executed written license agreement.' Derivatives are bound by the same non-commercial terms.

  ×No commercial optionNo fee is charged, but no commercial right is granted either: the JHU Academic Software License covers non-profit academic/research institutions only. A company would have to…

  Source checked 15 Oct 2026

• IgGM

  Antibody-specific design

  Generative model for antibody design that states commercial use is allowed.

  What it doesDesigns antibody sequences and structures together for a given antigen. Input: antigen structure plus the framework and the CDR positions to design; output: designed CDR sequences with the predicted antibody-antigen complex. Weights download automatically on first run.

  Tencent AI4S·Single GPU·Open weights

  ✓Commercial OKMIT License

  MIT LICENSE (Copyright 2024, Tencent Inc.) and an unusually direct README statement: 'Our model and code are released under MIT License, and can be freely used for both academic and commercial purposes.' That sentence covers the weights as well as the code, which is rare in this field. Note the same group's tFold is non-commercial - do not assume the licence carries across repos.

  0Free commerciallyFree: MIT, with the README stating free use for commercial purposes and weights fetched automatically at no charge.

  Source 1 Source 2 checked 15 Oct 2026

• ImmuneBuilder (ABodyBuilder2, NanoBodyBuilder2)

  Antibody-specific design

  Fast structure prediction for antibodies, nanobodies and TCRs.

  What it doesFast immune-receptor structure prediction. Input: heavy/light (ABodyBuilder2), nanobody (NanoBodyBuilder2) or TCR sequences; output: an all-atom Fv/VHH/TCR PDB in seconds, refined with OpenMM.

  OPIG, University of Oxford·Single GPU·Open weights

  ✓Commercial OKBSD 3-Clause License

  BSD 3-Clause LICENSE file in the repo; permissive, commercial use allowed.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source checked 15 Oct 2026

• mBER

  Antibody-specific design

  Open antibody binder design by backprop through AlphaFold-Multimer, format-specific.

  What it doesDesigns antibody binders in a chosen format. Input: target structure plus a framework/format template (VHH, Fab, scFv) and epitope; output: designed CDR sequences and predicted complexes, by optimising through AlphaFold-Multimer with sequence conditioning and structure templates.

  Manifold Bio·Heavy GPU·Open weights

  ✓Commercial OKMIT License

  MIT LICENSE file (Copyright 2025 Manifold Bio), and the README's License section repeats MIT. Caveat to check yourself: its download_weights.sh pulls roughly 9 GB of third-party weights (AlphaFold2, NanoBodyBuilder2, ESM-2) whose own terms apply - AlphaFold2 parameters are CC BY 4.0 and the others are permissive, which is why this still reads as a yes.

  0Free commerciallyNo fee: MIT code and freely downloadable third-party weights. Your cost is GPU time, which for backprop-through-AF2 design is the real expense.

  Source 1 Source 2 checked 15 Oct 2026

• RFantibody

  Antibody-specific design

  Pipeline for structure-based de novo antibody and nanobody design.

  What it doesDesigns antibody/nanobody CDR loops onto a real framework. Input: target PDB + epitope hotspots + a VHH or scFv framework; output: designed CDR backbones, then sequences via its ProteinMPNN step and a filter pass with RoseTTAFold2 antibody scoring.

  Institute for Protein Design, University of Washington (RosettaCommons)·Single GPU·Open weights

  ?UnverifiedMIT License

  The repo LICENSE is MIT, which covers the code. The README downloads model weights via include/download_weights.sh but states no separate license for those weights, so weight terms are unverified.

  0Free commerciallyNo fee is charged for the code (MIT) or the weight download script, but the weights carry no licence of their own, so the right to use them commercially is a separate, unresolved…

  Source 1 Source 2 checked 15 Oct 2026

• tFold

  Antibody-specific design

  Tencent's antibody structure prediction suite - PolyForm Noncommercial licence.

  What it doesPredicts antibody and nanobody structures, and antibody-antigen complexes, from sequence without MSAs. Input: heavy/light (or VHH) sequences, plus antigen sequence for the complex mode; output: predicted Fv or complex structures with confidence estimates.

  Tencent AI4S·Single GPU·Open weights

  ×Non-commercialPolyForm Noncommercial License 1.0.0

  The repo LICENSE is PolyForm Noncommercial 1.0.0, which grants rights only for noncommercial purposes. Contrast with the same lab's IgGM, which is MIT and explicitly commercial-friendly - read each repo's own file.

  ×No commercial optionNo fee and no commercial route: PolyForm Noncommercial grants noncommercial rights only, and no commercial licence is offered on the repo.

  Source checked 15 Oct 2026

• AlphaFold 2

  Structure and complex prediction

  Original AlphaFold; monomer and multimer structure prediction from sequence.

  What it doesPredicts protein structure from sequence. Input: FASTA (monomer or multimer); output: 5 ranked all-atom models with pLDDT and PAE, plus MSAs. Still the workhorse for binder-design filtering and the engine inside BindCraft/ColabFold.

  Google DeepMind·Heavy GPU·Open weights

  ✓Commercial OKApache License 2.0 (code); CC BY 4.0 (model parameters)

  README's 'Model Parameters License' section states parameters are made available under CC BY 4.0, which replaced the earlier CC BY-NC 4.0 terms; attribution required. Some mirrored MSA databases carry their own CC BY-SA 4.0 terms.

  0Free commerciallyFree: code Apache-2.0, parameters CC BY 4.0 (changed from CC BY-NC in January 2022). Attribution is the only cost.

  Source checked 15 Oct 2026

• AlphaFold 3

  Structure and complex prediction

  Predicts structures of proteins with ligands, nucleic acids and modifications.

  What it doesPredicts the structure of a complex. Input: a JSON listing protein/RNA/DNA chains, ligands, ions and modifications; output: an all-atom predicted complex with pLDDT, PAE and ranking scores. Code is public; weights must be requested and are non-commercial.

  Google DeepMind and Isomorphic Labs·Heavy GPU·Weights not openly downloadable

  !RestrictedApache License 2.0 (code); 'AlphaFold 3 Model Parameters Terms of Use' (weights)

  Code is Apache-2.0, but the README states you may only use the model parameters if received directly from Google, and the weights Terms of Use make them available free of charge only for certain non-commercial uses, by or on behalf of non-commercial organisations. Commercial organisations cannot use the released weights under those terms.

  ×No commercial optionThere is no price at which a company can use the AlphaFold 3 weights: the Model Parameters Terms of Use restrict them to non-commercial use by non-commercial organisations, and…

  Source 1 Source 2 +1 morechecked 15 Oct 2026

• Boltz-2

  Structure and complex prediction

  Open structure and binding-affinity prediction model, MIT for code and weights.

  What it doesOpen AlphaFold3-class structure prediction plus binding affinity. Input: YAML/FASTA with protein, ligand and nucleic-acid chains, optional templates and affinity query; output: predicted complex with confidence scores and a predicted binding affinity value for the ligand.

  Wohlwend, Corso, Passaro et al. (MIT CSAIL) / boltz.bio·Single GPU·Open weights

  ✓Commercial OKMIT License

  LICENSE file is MIT and the README states explicitly that all code and weights are MIT, freely usable for academic and commercial purposes.

  0Free commerciallyFree, and cleanly so: MIT covers both code and weights, which is why it is the usual commercial replacement for AlphaFold 3.

  Source 1 Source 2 checked 15 Oct 2026

• Chai-1

  Structure and complex prediction

  Multi-modal structure prediction for proteins, ligands, nucleic acids and glycans.

  What it doesStructure prediction for proteins with ligands, nucleic acids, glycans and modified residues. Input: FASTA plus optional restraints/MSAs; output: all-atom predicted complex with per-atom confidence - usable as an AlphaFold3 substitute whose weights are usable commercially.

  Chai Discovery·Single GPU·Open weights

  ✓Commercial OKApache License 2.0

  LICENSE file is Apache-2.0 and the README's Licence section states both code and model weights are Apache-2.0 and usable for academic and commercial purposes including drug discovery.

  0Free commerciallyFree to download and run commercially under Apache-2.0. Chai's hosted web platform is a separate product with its own terms; the open weights are what carry the free commercial…

  Source 1 Source 2 checked 15 Oct 2026

• ColabFold

  Structure and complex prediction

  Fast AlphaFold2 with MMseqs2-based MSAs, runnable in Colab.

  What it doesAlphaFold2/multimer with fast MSAs. Input: FASTA (or a Colab notebook cell); output: ranked predicted structures with pLDDT/PAE plots, using MMseqs2 remote MSA search instead of a local database.

  Mirdita, Ovchinnikov, Steinegger et al.·Single GPU·Open weights

  ✓Commercial OKMIT License

  MIT LICENSE file in the repo. The AlphaFold2 parameters it downloads are CC BY 4.0 per the AlphaFold README. Note the free public MSA server is intended for modest academic-scale use; heavy or commercial batch use should run a local MMseqs2 server.

  0Free commerciallyThe software is free (MIT) and the public MSA server costs nothing, though it is rate-limited and fair-use. Heavy or production use means running your own MSA server or paying…

  Source 1 Source 2 checked 15 Oct 2026

• ESMFold

  Structure and complex prediction

  Single-sequence structure prediction with no MSA required.

  What it doesMSA-free structure prediction. Input: a single protein sequence; output: an all-atom PDB with pLDDT in seconds-to-minutes, no MSA search needed. Good for screening thousands of designed sequences; weaker than AlphaFold2 on natural targets.

  Meta AI (FAIR) Evolutionary Scale Modeling·Single GPU·Open weights

  ✓Commercial OKMIT License

  MIT LICENSE file in the facebookresearch/esm repo covering the released code and models.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source 1 Source 2 checked 15 Oct 2026

• HelixFold3

  Structure and complex prediction

  Baidu's AlphaFold3 reproduction - code non-commercial, commercial use only via the paid server.

  What it doesPredicts all-atom biomolecular complexes (proteins, ligands, nucleic acids, ions). Input: a JSON entity list with optional MSAs; output: ranked all-atom complex structures with confidence scores. Built on PaddlePaddle, so it needs that stack rather than PyTorch.

  Baidu (PaddleHelix team)·Heavy GPU·Open weights

  !RestrictedCustom 'Terms of Use Agreement' - non-commercial use only for code and parameters

  The helixfold3 LICENSE states the open-source code and derivative works are 'only available for non-commercial use by individuals and non-commercial organizations... or for journalism', and the README repeats that code and model parameters are non-commercial only. The same README says the free version of the HelixFold3 server is non-commercial while 'the paid version offers unrestricted usage, enabling commercial applications' - so commercial use exists, but only as a paid hosted service, not as a self-hosted model.

  $Paid serviceSelf-hosting commercially is not permitted at any price. Commercial use runs through the paid tier of the hosted HelixFold3 server; the README states the free tier is…

  Source 1 Source 2 checked 15 Oct 2026

• OpenFold

  Structure and complex prediction

  Trainable open reimplementation of AlphaFold 2.

  What it doesTrainable AlphaFold2 reimplementation. Input: FASTA/MSAs; output: predicted structures, with the ability to fine-tune or retrain on your own data - which the original AlphaFold code does not give you.

  AlQuraishi lab, Columbia University·Heavy GPU·Open weights

  ✓Commercial OKApache License 2.0

  Apache-2.0 LICENSE file in the repo. The README notes DeepMind's pretrained parameters fall under CC BY 4.0, replacing the earlier CC BY-NC 4.0 terms; OpenFold's own trained weights are distributed with the project.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source 1 Source 2 checked 15 Oct 2026

• Protenix

  Structure and complex prediction

  Open AlphaFold3-class complex predictor from ByteDance, Apache-2.0 for code and v1 parameters.

  What it doesPredicts all-atom structures of biomolecular complexes. Input: a JSON/YAML of protein, nucleic-acid and ligand entities (optionally MSAs); output: ranked all-atom complexes with confidence scores. Also ships lightweight 'Mini' variants that cut inference cost.

  ByteDance (AML AI for Science)·Heavy GPU·Open weights

  ✓Commercial OKApache License 2.0 (code and released model parameters)

  The README's License section states the project, code and model parameters, is released under Apache 2.0 and is 'free for both academic research and commercial use'. Important exception read in the same README: the newer Protenix-v2 weights are described as proprietary and confidential, not released under any open-source licence - so the free commercial grant covers the openly released checkpoints, not v2.

  0Free commerciallyNo fee for the open checkpoints - the README states Apache 2.0 for code and parameters and explicitly says commercial use is free. Protenix-v2 is not available at any published…

  Source 1 Source 2 checked 15 Oct 2026

• AntiFold

  Sequence design / inverse folding

  Antibody-specific inverse folding, fine-tuned from ESM-IF1.

  What it doesDesigns antibody sequences for a given Fv backbone. Input: an antibody structure (PDB) with the regions to redesign, e.g. specific CDRs; output: per-position amino-acid probabilities and sampled sequences that keep the fold - the antibody-tuned counterpart to ProteinMPNN.

  OPIG (Oxford Protein Informatics Group) and collaborators·CPU·Open weights

  ✓Commercial OKBSD 3-Clause License (code and the released model weights)

  The repo LICENSE is BSD 3-Clause, and the README's model section links the weight download alongside an explicit 'License: BSD 3-Clause' line for those weights. One caveat read in the same README: an optional --esm_if1_mode runs ESM-IF1 weights instead, which carry Meta's own terms - check those if you use that flag.

  0Free commerciallyFree: BSD 3-Clause for both code and weights, no fee.

  Source 1 Source 2 checked 15 Oct 2026

• LigandMPNN

  Sequence design / inverse folding

  Inverse folding that is aware of ligands, nucleotides and metals.

  What it doesInverse folding that sees the non-protein context. Input: a PDB containing the protein plus ligands, nucleotides, metals or waters; output: sequences designed around that context, with side-chain packing - the right choice for binding pockets and metal sites.

  Justas Dauparas, Institute for Protein Design, University of Washington·Single GPU·Open weights

  ✓Commercial OKMIT License

  MIT LICENSE file in the repo; no field-of-use restriction.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source checked 15 Oct 2026

• ProteinMPNN

  Sequence design / inverse folding

  Designs amino-acid sequences that fold to a given backbone.

  What it doesInverse folding. Input: a backbone PDB (optionally with fixed positions, chain bias, symmetry); output: amino-acid sequences predicted to fold to that backbone, with per-residue probabilities and scores. CPU-fast; the standard step after any backbone generator.

  Justas Dauparas, Institute for Protein Design, University of Washington·CPU·Open weights

  ✓Commercial OKMIT License

  MIT LICENSE file in the repo; no field-of-use restriction.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source checked 15 Oct 2026

• AbLang2

  Protein language models

  Antibody-specific language model for paired heavy/light sequences.

  What it doesAntibody-specific language model. Input: a paired heavy+light (or single-chain) antibody sequence; output: embeddings and residue likelihoods used to score naturalness, restore missing residues and rank humanisation or affinity-maturation edits.

  OPIG, University of Oxford·Single GPU·Open weights

  ✓Commercial OKBSD 3-Clause License

  BSD 3-Clause LICENSE file in the repo; permissive, attribution and no-endorsement conditions only.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source checked 15 Oct 2026

• ESM C / ESMFold2 (esm package)

  Protein language models

  Newer ESM protein world-model family with folding, now MIT licensed.

  What it doesNewer ESM encoder family (ESM C). Input: a protein sequence; output: embeddings and likelihoods at better quality-per-parameter than ESM-2, as a drop-in replacement for representation and scoring workloads.

  EvolutionaryScale / Chan Zuckerberg Biohub·Heavy GPU·Open weights

  ✓Commercial OKMIT License

  LICENSE.md in the repo is MIT (Copyright 2026 Chan Zuckerberg Biohub) and the README's Licenses section states the models are available under the MIT license. Note this differs from the earlier ESM3 non-commercial community license; verify the checkpoint you actually download.

  0Free commerciallyFree: the esm repository's licence file is MIT (Copyright 2026 Chan Zuckerberg Biohub) and the open checkpoints are downloadable without payment. The maintainers also sell hosted…

  Source 1 Source 2 checked 15 Oct 2026

• ESM-2

  Protein language models

  Protein language model for embeddings, fine-tuning and variant scoring.

  What it doesProtein language model. Input: a protein sequence; output: per-residue and per-sequence embeddings, masked-token likelihoods and variant pseudo-likelihood scores - the standard feature extractor for fitness/liability models and design filters.

  Meta AI (FAIR)·Single GPU·Open weights

  ✓Commercial OKMIT License

  The Hugging Face model card front-matter declares 'license: mit' for the released weights; the source repo LICENSE is also MIT.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source 1 Source 2 checked 15 Oct 2026

• ESM3

  Protein language models

  Generative multimodal protein model reasoning jointly over sequence, structure and function.

  What it doesGenerates and edits proteins across three tracks at once. Input: any partial specification - sequence, backbone coordinates, secondary structure, SASA or function keywords, with the rest masked; output: the completed sequence and/or structure. Used for prompted generation and scaffolding rather than plain embedding extraction.

  EvolutionaryScale (repository now copyright Chan Zuckerberg Biohub)·Heavy GPU·Open weights

  ✓Commercial OKMIT License (LICENSE.md, Copyright 2026 Chan Zuckerberg Biohub, Inc.)

  The repository LICENSE.md is MIT and the README's Licenses section states 'These models are available under the MIT license' - a change from the earlier non-commercial Cambrian open licence. Larger checkpoints remain behind a hosted API; the open ones are the MIT-licensed download.

  0Free commerciallyFree for the open checkpoints under MIT. The vendor's hosted API for the larger models is a separate paid service you are not required to use.

  Source 1 Source 2 checked 15 Oct 2026

• Evo 2

  Protein language models

  Genome-scale DNA/RNA/protein foundation model, Apache-2.0 including weights.

  What it doesModels biological sequence at the nucleotide level across very long contexts. Input: DNA, RNA or protein sequence (up to ~1 Mb context); output: likelihoods for variant-effect prediction, embeddings, or generated sequence. Relevant to design work for variant scoring and regulatory/coding sequence generation rather than structure.

  Arc Institute, with NVIDIA and Stanford·Heavy GPU·Open weights

  ✓Commercial OKApache License 2.0

  Repo LICENSE is Apache-2.0 and the Hugging Face checkpoints (arcinstitute/evo2_7b and siblings) declare apache-2.0 in their card metadata, so code and weights are both permissive. The published checkpoints are ungated.

  0Free commerciallyFree: Apache-2.0 code and weights with no fee. NVIDIA also sells hosted inference, which is optional.

  Source 1 Source 2 checked 15 Oct 2026

• IgBert / IgT5

  Protein language models

  Exscientia's paired-antibody BERT and T5 models, MIT licensed on Hugging Face.

  What it doesEncode or generate antibody sequences with paired heavy+light context. Input: heavy and light chain sequences (space-separated residues, paired or unpaired); output: per-residue and sequence embeddings, masked-residue predictions (IgBert) or sequence-to-sequence generation (IgT5), for naturalness scoring, humanness work and property prediction.

  Exscientia·Single GPU·Open weights

  ✓Commercial OKMIT License (as declared on the model cards)

  Both model repositories declare the MIT licence in their card metadata on Hugging Face, covering the published weights; the models are ungated and download without an access request. Verified against the Hugging Face model API for Exscientia/IgBert and Exscientia/IgT5.

  0Free commerciallyFree: MIT-declared weights, public download, no access request and no fee.

  Source 1 Source 2 checked 15 Oct 2026

• IgLM

  Protein language models

  Antibody-specific generative language model - academic licence only.

  What it doesGenerates and infills antibody sequences conditioned on species and chain type. Input: a partial antibody sequence with a span to fill, plus species/chain tokens; output: completed sequences with likelihoods, used for library generation and for scoring how natural a designed CDR looks.

  Gray Lab, Johns Hopkins University·Single GPU·Open weights

  ×Non-commercialJHU Academic Software License Agreement

  LICENSE.md is the JHU Academic Software License Agreement, effective only between JHU and 'a non-profit academic or non-profit research institution'. Same pattern as IgFold from the same lab. A company needs a separate agreement with JHU.

  ×No commercial optionNo fee for academics; no published commercial price. Commercial use would require negotiating with JHU's technology transfer office.

  Source checked 15 Oct 2026

• ProGen3

  Protein language models

  Profluent's sparse protein language model family - code open, weights non-commercial.

  What it doesGenerates and scores protein sequences at scale. Input: a prompt (family/function tags or a partial sequence) for generation, or sequences for scoring; output: generated sequences, or log-likelihood fitness scores used to rank variants. Ships as a CLI for scoring and generation.

  Profluent Bio·Heavy GPU·Open weights

  ×Non-commercialApache License 2.0 (code, LICENSE-CODE); CC BY-NC-SA 4.0 (model weights)

  The README states up front that 'the model weights are released under a non-commercial usage license', and its LICENSE section splits them: Apache-2.0 for the code in LICENSE-CODE, CC BY-NC-SA 4.0 for the weights. The Hugging Face model card metadata also declares cc-by-nc-sa-4.0. The code being Apache-2.0 does not help: without the weights there is no model.

  ×No commercial optionNo fee, but also no commercial grant: CC BY-NC-SA 4.0 excludes commercial use and Profluent publishes no commercial licence price for the weights.

  Source 1 Source 2 +1 morechecked 15 Oct 2026

• ProtGPT2

  Protein language models

  GPT-2-style generative model that writes de novo protein sequences.

  What it doesGenerates novel protein sequences unconditionally or from a seed. Input: an optional sequence prefix and sampling parameters; output: new sequences in natural-protein-like space, plus perplexity scores. A sequence-only generator: it says nothing about structure, so fold it afterwards.

  Noelia Ferruz et al.·Single GPU·Open weights

  ✓Commercial OKApache License 2.0 (as declared on the model card)

  The Hugging Face model card metadata declares apache-2.0 for the published weights, and the model is ungated. Verified against the Hugging Face model API.

  0Free commerciallyFree: Apache-2.0 weights, public download, no fee.

  Source checked 15 Oct 2026

• AutoDock Vina

  Docking and affinity estimation

  Classic small-molecule docking with an empirical scoring function.

  What it doesSmall-molecule docking. Input: receptor PDBQT + ligand + search box; output: docked poses with an empirical affinity score - the baseline for pocket-level triage, not for protein-protein work.

  Scripps Research (CCSB)·CPU·Open weights

  ✓Commercial OKApache License 2.0

  Apache-2.0 LICENSE file in the repo; permissive.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source checked 15 Oct 2026

• GNINA

  Docking and affinity estimation

  Molecular docking with CNN-based pose scoring.

  What it doesDocking with a CNN rescorer. Input: receptor + ligand (and optionally a reference pose); output: poses scored by both the Vina-style function and a convolutional neural network, which usually improves pose ranking.

  Koes lab, University of Pittsburgh·Single GPU·Open weights

  ✓Commercial OKApache License 2.0 (LICENSE.APACHE)

  The repo's license file LICENSE.APACHE is Apache-2.0, reported by the GitHub license API. Bundled dependencies (e.g. OpenBabel) may carry their own copyleft terms and should be checked before redistribution.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source 1 Source 2 checked 15 Oct 2026

• PRODIGY

  Docking and affinity estimation

  Estimates binding affinity of protein-protein complexes from structure.

  What it doesAffinity estimate for a protein-protein complex. Input: a complex structure (PDB); output: a predicted binding affinity (dG/Kd estimate) from interfacial contact composition - a structure-based ranking signal, not a measurement.

  Bonvin lab, Utrecht University·CPU·Open weights

  ✓Commercial OKApache License 2.0

  Apache-2.0 LICENSE file reported by the GitHub license API for haddocking/prodigy.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source 1 Source 2 checked 15 Oct 2026

• Humatch

  Developability and sequence-liability prediction

  Humanness scoring and humanisation guidance for antibody sequences.

  What it doesHumanness scoring and humanisation. Input: heavy/light antibody sequences; output: a humanness score per chain plus suggested germline-directed mutations, so you can de-risk a non-human design before ordering it.

  OPIG, University of Oxford·CPU·Open weights

  ✓Commercial OKBSD 3-Clause License

  BSD 3-Clause LICENSE file on the repo's master branch; permissive.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source 1 Source 2 checked 15 Oct 2026

• NetSolP

  Developability and sequence-liability prediction

  Predicts solubility and usability of proteins from sequence.

  What it doesSolubility and expression risk. Input: protein sequences (FASTA); output: predicted solubility and 'usability' probabilities per sequence, for triaging designs before paying for expression.

  DTU Health Tech (Technical University of Denmark)·Single GPU·Weights: unverified

  ✓Commercial OKBSD 3-Clause License

  BSD 3-Clause LICENSE file in the repo (Copyright 2021, Technical University of Denmark); permissive.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source checked 15 Oct 2026

• TAP (Therapeutic Antibody Profiler)

  Developability and sequence-liability prediction

  Flags antibody developability risks against clinical-stage antibody ranges.

  What it doesDevelopability red flags. Input: an antibody Fv structure (or sequence to be modelled); output: five TAP metrics - CDR length, patches of surface hydrophobicity, positive and negative charge, and charge asymmetry - each placed against the distribution of clinical-stage antibodies.

  OPIG, University of Oxford (SAbPred web server)·Hosted service·Weights: unverified

  ?Unverifiedunverified

  The SAbPred TAP web page states no license or terms of use, and no public source repository license was found; terms for commercial use are therefore unverified.

  ?Cost unverifiedThe TAP web server is offered for free use through OPIG, but no licence or terms page was found that states whether a company may use it, so neither the right nor any commercial…

  Source 1 Source 2 checked 15 Oct 2026

• ThermoMPNN

  Developability and sequence-liability prediction

  Predicts stability change (ddG) for single point mutations.

  What it doesStability effect of point mutations. Input: a protein structure (PDB); output: predicted ddG for single-point mutations (one site or the full saturation scan), used to pick stabilising mutations for a designed scaffold.

  Kuhlman lab, UNC Chapel Hill·Single GPU·Open weights

  ✓Commercial OKMIT License

  MIT LICENSE file in the repo.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source checked 15 Oct 2026

• GROMACS

  MD and refinement

  High-performance molecular dynamics engine for biomolecular simulation.

  What it doesProduction MD engine. Input: structure + topology + run parameters; output: long trajectories with free-energy and analysis tooling - for stability, aggregation and interface-dynamics studies of a design.

  GROMACS development team (KTH, Uppsala and others)·Heavy GPU·Open weights

  ✓Commercial OKGNU Lesser General Public License v2.1

  The COPYING file in the official GitLab repository is LGPL v2.1. Commercial use is permitted; distributing modified GROMACS libraries triggers LGPL source-sharing obligations.

  0Free commerciallyFree under LGPL-2.1. No fee; if you distribute a modified GROMACS you must release those modifications under the same terms.

  Source checked 15 Oct 2026

• OpenMM

  MD and refinement

  GPU-accelerated molecular dynamics toolkit with a Python API.

  What it doesMolecular dynamics engine with a Python API. Input: a structure plus a force field; output: trajectories, energies and minimised structures. Used inside design pipelines for relaxation and side-chain/loop refinement.

  Stanford University and the OpenMM community·Single GPU·Open weights

  ✓Commercial OKMixed: MIT for most of OpenMM, with some parts under LGPL (see Licenses.txt)

  Licenses.txt states most of OpenMM is under the MIT License while certain portions are under the LGPL (with GPL referenced for those parts). Commercial use is allowed; redistribution of the LGPL portions carries LGPL obligations.

  0Free commerciallyFree. Most of OpenMM is MIT; some components are LGPL, which costs nothing but obliges you to allow relinking if you distribute a modified build.

  Source checked 15 Oct 2026

• PyRosetta

  MD and refinement

  Python interface to Rosetta for protein modelling and design.

  What it doesRosetta's Python API. Input: structures and movers you script; output: relaxed/minimised and scored structures, interface metrics (ddG), loop remodelling and packing. Dependency of several design pipelines.

  RosettaCommons / University of Washington·CPU·Open weights

  !RestrictedRosettaCommons license (free academic/non-profit; paid commercial)

  The official licensing page states licensing is free for academic and non-profit institutions and available to commercial users for a fee, handled through Rosetta Commons. A commercial entity needs a paid license.

  $Paid licenceFree for academic and non-profit users; commercial users pay a licence fee, quoted by RosettaCommons/UW on request rather than published as a list price. This is the single most…

  Source checked 15 Oct 2026

• AlphaFold Protein Structure Database

  Data and structure resources

  Precomputed AlphaFold structure predictions for most known proteins.

  What it doesPrecomputed structure predictions. Input: a UniProt accession; output: the AlphaFold model with pLDDT/PAE - the fastest way to get a usable structure for a target with no crystal.

  Google DeepMind and EMBL-EBI·Hosted service·Open weights

  ✓Commercial OKCreative Commons Attribution 4.0 International (CC BY 4.0)

  The AlphaFold DB homepage and download page both state: 'Data is available for academic and commercial use, under a CC-BY-4.0 licence.'

  0Free commerciallyFree under CC BY 4.0 - note this is the database of predictions, which is licensed far more openly than the AlphaFold 3 weights.

  Source 1 Source 2 checked 15 Oct 2026

• Foldseek

  Data and structure resources

  Fast structural similarity search across millions of structures.

  What it doesStructure search. Input: a query structure; output: structurally similar entries across PDB/AFDB with alignment and E-values - how you check whether a designed fold or epitope already exists.

  Steinegger lab, Seoul National University·CPU·Open weights

  !RestrictedGNU General Public License v3.0

  LICENSE.md in the repo is GPL-3.0. Internal commercial use is allowed, but distributing software that incorporates Foldseek requires releasing the derivative work under GPL-3.0, which matters if you plan to ship a product containing it.

  0Free commerciallyNo fee, but GPL-3.0: if you ship Foldseek inside a product you distribute, you must release that product's source under GPL-3.0. Running it internally as a service carries no…

  Source 1 Source 2 checked 15 Oct 2026

• MMseqs2

  Data and structure resources

  Very fast sequence search and clustering, the MSA engine behind ColabFold.

  What it doesSequence search and clustering at scale. Input: query and target FASTA/databases; output: homology hits, clusters and MSA profiles in minutes where BLAST takes days - the MSA engine behind ColabFold.

  Steinegger and Söding labs·CPU·Open weights

  ✓Commercial OKMIT License

  LICENSE.md in the repo is 'The MIT License (MIT)'.

  0Free commerciallyNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.

  Source checked 15 Oct 2026

• Observed Antibody Space (OAS)

  Data and structure resources

  Over a billion annotated antibody repertoire sequences, paired and unpaired.

  What it doesAntibody repertoire sequences. Input: a query over studies/species/chains; output: over a billion annotated BCR sequences, paired and unpaired, for training language models and checking whether a CDR looks natural.

  OPIG, University of Oxford·Hosted service·Open weights

  ✓Commercial OKCreative Commons Attribution 4.0 International (CC BY 4.0)

  The OAS homepage states: 'Please note that the data contained within OAS is available under a CC-BY 4.0 license.'

  0Free commerciallyFree under CC BY 4.0, including for commercial training runs; attribute OAS and the underlying studies.

  Source checked 15 Oct 2026

• Open-Source PyMOL

  Data and structure resources

  Molecular visualisation for inspecting structures and interfaces.

  What it doesMolecular visualisation and measurement. Input: structures and trajectories; output: rendered images/sessions plus interface inspection - contacts, distances, buried surface - for sanity-checking a design by eye.

  Schrödinger, LLC (open-source build)·CPU·Open weights

  ✓Commercial OKOpen-Source PyMOL Copyright Notice (custom permissive, MIT/X11-style)

  The LICENSE file grants permission to use, copy, modify and distribute for any purpose without fee, provided copyright notices are kept; the PyMOL name may not be used in advertising without permission, and derivatives must be plainly distinguished from Schrödinger's PyMOL products. The paid Incentive PyMOL product is separate.

  0Free commerciallyThe open-source build is free for any use. The commercial PyMOL sold by Schrodinger is a separate, paid product; you do not need it, and incentive/educational builds have their…

  Source checked 15 Oct 2026

• Protein Data Bank (RCSB PDB)

  Data and structure resources

  The archive of experimentally determined biomolecular structures.

  What it doesPrimary structure archive. Input: a PDB ID or search query; output: experimentally determined coordinates and metadata - the source of your target structure and of nearly every training set in this list.

  wwPDB / RCSB PDB·Hosted service·Open weights

  ✓Commercial OKCC0 1.0 Universal (archive data)

  The RCSB usage policy page states the wwPDB policy makes PDB archive data files available under CC0 1.0, as is data from RCSB programmatic APIs. RCSB software products are separately under MIT or Apache-2.0.

  0Free commerciallyFree, CC0: no fee, no attribution requirement (citation is requested as courtesy). Bulk download via FTP/rsync is open.

  Source checked 15 Oct 2026

• SAbDab

  Data and structure resources

  Curated, annotated database of antibody structures from the PDB.

  What it doesAntibody structure database. Input: search by antigen, species, CDR length, affinity or date; output: curated, numbered antibody/nanobody structures with annotations - the benchmark set behind antibody design papers.

  OPIG, University of Oxford·Hosted service·Open weights

  ✓Commercial OKCreative Commons Attribution 4.0 International (CC BY 4.0)

  The SAbDab site states the data is made available under CC BY 4.0 (link to creativecommons.org/licenses/by/4.0/ in the site's about text); attribution required.

  0Free commerciallyFree to download and use commercially under CC BY 4.0; the cost is attribution.

  Source 1 Source 2 checked 15 Oct 2026

• UniProt

  Data and structure resources

  Reference protein sequence and functional annotation database.

  What it doesReference sequences and annotation. Input: accession, gene or text query; output: canonical sequences, isoforms, domains, PTMs and functional annotation - the identity layer for any target you design against.

  UniProt Consortium (EMBL-EBI, SIB, PIR)·Hosted service·Open weights

  ✓Commercial OKCreative Commons Attribution 4.0 International (CC BY 4.0)

  The UniProt license help page states CC BY 4.0 applies to all copyrightable parts of their databases, with a disclaimer that some data may be covered by patents or other rights.

  0Free commerciallyFree under CC BY 4.0, bulk downloads included.

  Source 1 Source 2 checked 15 Oct 2026

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