Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types.
For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).
SKILL.md
Binder Design Tool Selection
Decision tree
De novo binder design?
│
├─ Standard target → BoltzGen (recommended)
│ All-atom output (no separate ProteinMPNN step needed)
│ Better for ligand/small molecule binding
│ Single-step design (backbone + sequence + side chains)
│
├─ Need diversity/exploration → RFdiffusion + ProteinMPNN
│ Maximum backbone diversity
│ Two-step: backbone then sequence
│
├─ Integrated validation → BindCraft
│ Built-in AF2 validation
│ End-to-end pipeline
│
├─ Ligand binding → BoltzGen ✓
│ All-atom diffusion handles ligand context
│
├─ Peptide/nanobody → Germinal
│ VHH/nanobody design
│ Germline-aware optimization
│
└─ Antibody/Nanobody
+-- VHH design --> germinal skill
Tool comparison
Tool
Strengths
Weaknesses
Best For
BoltzGen
All-atom, single-step, ligand-aware
Higher GPU requirement
Standard (recommended)
BindCraft
End-to-end, built-in AF2 validation
Less diverse
Production campaigns
RFdiffusion
High diversity, fast
Requires ProteinMPNN
Exploration, diversity
Germinal
Nanobody/VHH design
Specialized
Antibody optimization
Recommended Pipeline: BoltzGen → Chai → QC
BoltzGen provides all-atom design with built-in side-chain packing: