Design binders against your target.

You have a target structure and know roughly which patch of its surface you want gripped, and you want brand-new mini-proteins of 60 to 150 residues built to grip it. Every candidate is refolded and filtered before you see it, so what comes back is already a shortlist.

You have a target and have not settled on what shape the binder should be. One model here aims mini-proteins, nanobodies, antibodies or peptides at the same site, so you can compare formats instead of guessing. The only design tool here that handles sugars and modified residues on the target natively.

You want a paired heavy + light scFv — a single-chain antibody fragment — with all six binding loops designed against your target at once, which no other tool here does. It also builds small de novo binders by a different route to RFdiffusion, worth a run when a target has gone quiet. For single-domain nanobodies use RFantibody.

You have an antibody or nanobody and an antigen, and you want to redesign its binding loops, humanise its framework, raise its affinity, or just see how the two dock — one model does all of it, aimed at the epitope you name. For a nanobody from scratch use RFantibody; for a paired heavy and light antibody fragment use ESMFold2 design.

You have a hard target — a recessed pocket, a site spanning two chains, or a small molecule rather than a protein — and you want to throw as much search at it as your balance allows. Every candidate is re-folded and scored against your target as it is generated, and the run fans out across as many GPUs as you fund.

You have a target and you want every single candidate to arrive with a real AlphaFold2 confidence score against that target, not a cheaper stand-in. This is the pipeline Ranomics runs for its own wet-lab campaigns. For design without that filtering step use BindCraft; for antibody formats use RFantibody or IgGM.

You have a target structure and want nanobodies — single-domain antibodies you can carry straight into yeast display, mammalian display or a hybridoma workflow. Each candidate is refolded and scored against the target before you see it. For non-antibody mini-proteins use RFdiffusion or BindCraft; for sugar-coated targets use BoltzGen.

You have a target structure and a patch of its surface you want gripped, and you want brand-new binders of whatever shape works — the general-purpose starting point for de novo design. Every candidate comes back with a real AlphaFold2 confidence score against your own target. For antibody or nanobody formats use RFantibody or IgGM instead.

You have a backbone — a 3D shape with no sequence decided yet — and need amino-acid sequences that will fold into it. Ranked candidates come back in about 30 seconds. To generate the backbone in the first place, run a binder design tool and feed its PDB in here.

You have a sequence and want the most trusted 3D prediction of it, with per-residue confidence you can act on. It searches for related natural sequences first, which is where the accuracy comes from and where the time goes. For a faster answer use ColabFold or ESMFold; to score a binder against its target use Boltz-2.

You already have a binder sequence and the target it should hit, and you want to know whether they actually stick together before you order DNA. Returns the predicted complex and a 0-to-1 interface confidence score. Trained on antibody-antigen complexes, so it is a genuinely second opinion next to AlphaFold2.

You have a sequence and want its 3D shape in a minute or two, trading a little accuracy for speed. It skips the search for related natural sequences that full AlphaFold2 runs. Use it to triage a batch; use AlphaFold2 when the answer has to be right.

You have one protein sequence and want its 3D shape in about 30 seconds. No search for relatives, so it works on designed or orphan sequences that have no natural family to align against. One chain only — for complexes use ColabFold or AlphaFold2.

You have a complex that is not all protein — protein with DNA, with RNA, or with a bound small molecule — and you want the whole thing folded together in one prediction. For a plain protein-protein or protein-peptide complex, Boltz-2 is faster and cheaper.

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