Binder Design Tool Selection Skill

Binder Design Tool Selection

Which tool wins

No single tool is best for every target. Hit-rate is strongly target-dependent, so choose by target type, what you want to control, and available compute.

The clearest signal comes from head-to-head competitions where many methods design against the same target. On the Adaptyv Nipah de novo target, the public results show:

| Method | Tested | Binders | Hit-rate | |--------|--------|---------|----------| | Mosaic (gradient, multi-model) | 9 | 8 | 89% | | ProteinMPNN hybrid | 28 | 7 | 25% | | RFdiffusion | 60 | 13 | 22% | | BindCraft | 98 | 7 | 7% | | BoltzGen | 182 | 6 | 3% |

Mosaic had the highest hit-rate here, but on a small, expert-tuned sample. The ranking shifts on other targets, and that target-dependence is true of every method (BoltzGen, Boltz, BindCraft, Mosaic). You cannot know a priori which will win on a new target, so this is not a fixed leaderboard.

Because of that, choose a starting point by cost and effort to a binder, not by assuming a method has the best hit-rate. BoltzGen is the suggested default because it is turnkey and all-atom, so it gets you testable designs fastest with the least setup. Mosaic is the high-ceiling option when you can invest time tuning the objective. On a hard or important target, running more than one method in parallel is reasonable.

De novo binder design?
│
├─ Lowest cost/effort to testable designs → BoltzGen (default)
├─ Hard/important target, can invest tuning → Mosaic (gradient, multi-model)
├─ Ligand / small-molecule binding → BoltzGen (all-atom)
├─ Diversity / exploration → RFdiffusion + ProteinMPNN
├─ End-to-end with built-in validation → BindCraft
└─ Antibody / nanobody (VHH) → germinal skill (also mber, iggm in biomodals)

Tool comparison

| Tool | Strengths | Weaknesses | Best for | |------|-----------|------------|----------| | BoltzGen | All-atom, single-step, turnkey | One model in the loop; mid-range cost per design | Lowest-effort default, ligand binding | | Mosaic | Composable multi-model objective, won hard head-to-heads | Needs tuning, local JAX only | Hard or important targets, expert use | | BindCraft | End-to-end, built-in AF2 validation | Less diverse | Production campaigns | | RFdiffusion | High diversity | Requires ProteinMPNN; not in biomodals | Exploration, diversity | | Germinal | Antibody and nanobody formats | Finicky | scFv / VHH design |

Compute cost per design

Adaptyv's own tests of these models showed the following compute cost per accepted design, averaged across 7 targets (it varies several-fold by target):

| Method | Cost per design | |--------|-----------------| | RSO | ~$0.15 | | RFdiffusion | ~$0.25 | | Mosaic | ~$0.55 | | ESMFold2 inversion | ~$0.85 | | mBER | ~$1.40 | | Germinal | ~$1.60 | | BoltzGen | ~$1.80 | | BindCraft | ~$2.90 |

Per-design compute cost is not the same as cost to a binder, which also depends on the hit-rate on your target. The gradient methods (RSO, Mosaic) are cheap per design but need setup and tuning; BoltzGen and BindCraft cost more per design but are turnkey, so their advantage is low human effort rather than lowest compute cost.

Compute vs effort tradeoff

Lowest human effort: BoltzGen needs no tuning and runs through biomodals. Good first pass and good for ligand binding.
Highest ceiling on a hard target: Mosaic, given time to design and tune the objective. It runs locally on a JAX GPU rather than through biomodals, and is cheap per design.
Whatever the generator, validate with boltz or chai and rank with ipsae.

Other biomodals-backed options: modal_rso.py (Rejection Sampling Optimization, an AlphaFold-based gradient method) for minibinders, and modal_mber.py for VHH nanobodies.

Example pipeline: BoltzGen → Chai → QC

BoltzGen provides all-atom design with built-in side-chain packing. This is one turnkey path; swap in Mosaic, RFdiffusion, or BindCraft depending on the target.

Target → BoltzGen → Validate → Filter
 (pdb)  (all-atom)   (chai)     (qc)

1. Target preparation

# Fetch structure from PDB
# Use pdb skill for guidance

Trim to binding region + 10A buffer
Remove waters and ligands
Renumber chains if needed

2. Hotspot selection

Choose 3-6 exposed residues
Prefer charged/aromatic residues
Cluster spatially (within 10-15A)

3. Design with BoltzGen

First, create a YAML config file (e.g., binder.yaml):

entities:
  - protein:
      id: B
      sequence: 70..100

  - file:
      path: target.cif
      include:
        - chain:
            id: A
      binding_types:
        - chain:
            id: A
            binding: 45,67,89

Then run:

modal run modal_boltzgen.py \
  --input-yaml binder.yaml \
  --protocol protein-anything \
  --num-designs 50

Why BoltzGen?

All-atom output (no separate ProteinMPNN step needed)
Better for ligand/small molecule binding
Single-step design (backbone + sequence + side chains)

4. Alternative: RFdiffusion Pipeline

For maximum diversity or when backbone-only is preferred:

# Step 1: Backbone generation (RFdiffusion, run from the official repo)
python run_inference.py \
  inference.input_pdb=target.pdb \
  contigmap.contigs=[A1-150/0 70-100] \
  ppi.hotspot_res=[A45,A67,A89] \
  inference.num_designs=500

# Step 2: Sequence design
modal run modal_ligandmpnn.py \
  --input-pdb backbone.pdb \
  --params-str "--number_of_batches 16 --temperature 0.1"

5. Validation

modal run modal_chai1.py \
  --input-faa sequences.fasta \
  --out-dir predictions/

6. Filtering

Apply standard thresholds:

pLDDT > 0.80
ipTM > 0.50
PAE_interface < 10
scRMSD < 2.0 A

See protein-qc skill for details.

Number of designs

| Stage | Count | Purpose | |-------|-------|---------| | Backbone generation | 500-1000 | Diversity | | Sequences per backbone | 8-16 | Sequence space | | AF2 predictions | All | Validation | | After filtering | 50-200 | Candidates | | Experimental testing | 10-50 | Final selection |

Common mistakes

Wrong hotspots

Using buried residues
Too many hotspots (over-constrain)
Wrong chain/residue numbers

Insufficient diversity

Too few designs generated
Low temperature in ProteinMPNN
Not exploring multiple backbones

Poor target preparation

Including full protein instead of binding region
Missing important structural features
Wrong protonation states

Timeline guide

| Step | Compute Time | |------|--------------| | RFdiffusion (500 designs) | 2-4 hours | | ProteinMPNN (8000 sequences) | 1-2 hours | | AF2 prediction (8000 sequences) | 12-24 hours | | Filtering and analysis | 1-2 hours |

Total: 1-2 days of compute

Agent Skills: Binder Design Tool Selection

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