Design binders against your target.

Pick Epitope Scout first to identify candidate epitopes and per-dimension feasibility for any target.

Pick the Library Planner when you have a binder design shortlist and need to scope library size, diversification scheme, and screening throughput before ordering DNA.

Pick BindCraft when you have a target PDB plus known hotspot residues and want de novo 60 to 150 aa protein binders.

Pick BoltzGen when you want one model that can design mini-proteins, nanobodies, antibodies, or peptides against the same target, or when your target involves glycans, post-translational modifications, or non-canonical residues.

Gradient-based binder design via ESMFold2 inversion. Pick a paper-validated target preset, choose minibinder or scFv mode, and get ranked designs with iPTM scores in one model pass.

Pick IgGM to design or humanize an antibody / nanobody against your antigen, or to predict the antibody-antigen complex, all in one model. For VHH backbones use RFantibody; for paired scFv CDRs use ESMFold2 design; to validate a designed binder's fold use Boltz-2.

Pick PXDesign when AF2 confidence against a defined target matters and you want real ipTM, pLDDT, and pAE on every candidate. For hallucination-driven binder design without AF2 filtering use BindCraft, for antibody and nanobody CDRs use RFantibody, and for target structure generation without binder design use BoltzGen.

Pick RFantibody when you need a VHH (nanobody) scaffold against a target PDB. For de novo non-antibody binders, use BindCraft. For designs involving modified residues or glycans, use BoltzGen.

Pick RFdiffusion when you want general de novo binder design scored by AF2 multimer (ipTM, pLDDT, i_pAE). For antibody and nanobody scaffolds use RFantibody, for AF2-IG initial-guess scoring use PXDesign, and for hallucination-driven binders without AF2 filtering use BindCraft.

Pick ProteinMPNN when you already have a backbone and need candidate sequences. For de novo backbone generation, use RFantibody, BindCraft, or BoltzGen first and feed the output PDB here.

Pick AF2 when you need the gold-standard structure prediction with calibrated pLDDT and PAE. For faster single-sequence folds use ESMFold (D4); for affinity-aware folds use Boltz-2 (D6).

Pick Boltz-2 to validate a designed binder against your antigen. Single-sequence cofold with interface confidence (ipTM), antibody-trained and orthogonal to AF2-multimer. For sequence design, use ProteinMPNN; for de novo backbones, use RFantibody, BindCraft, or BoltzGen first.

Pick ColabFold when you need a fast no-MSA fold. 1 to 2 min per run, no MMseqs2 round-trip. Pair with AF2 standalone (D2) when you want full MSA and templates, or with ESMFold (D4) for single-sequence monomers on an even smaller GPU.

Pick ESMFold when you need the fastest possible monomer fold. No MSA, no multimer, single-sequence ESM-2 language-model prediction. Pair with ColabFold (D3) for multimers or AF2 standalone (D2) for full MSA-backed accuracy.

Pick Developability Scout when you have a sequence and need a quick liability scan (CDR length, hydrophobic patches, charge balance, isoelectric point).

Pick OpenDDE to co-fold a mixed complex — protein with DNA, RNA, or a bound ligand in one prediction. For a plain protein-protein or protein-peptide cofold, Boltz-2 is faster and cheaper.

Pick Proteina-Complexa when you want de novo binders against a protein OR a small-molecule (ligand) target, scored by a full AF2 / RF3 / force-field reward stack, and you want to scale the search across many GPUs with the wallet as the only ceiling.