Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add GPTomics/bioSkills --skill ml-docking-rescoringgit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gptomics/bioskills/ml-docking-rescoring)<a href="https://agentmods.dev/skills/gptomics/bioskills/ml-docking-rescoring"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/ml-docking-rescoring/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gptomics/bioskills/ml-docking-rescoring"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/ml-docking-rescoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.04151 |
| Opus 5.5 | $0.00000 | $0.01660 |
| Sonnet 5.5 | $0.00000 | $0.00830 |
| Haiku 4.5 | $0.00000 | $0.00415 |
Grade A, and why
bio-ml-docking-rescoring scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-ml-docking-rescoring — 94% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 277 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: DiffDock-L (Corso et al. 2024), Boltz-1 1.0+, Boltz-2 (Passaro et al. 2025), Chai-1 0.4+, AlphaFold 3 (DeepMind), EquiBind, TANKBind, GNINA 1.1+, and PoseBusters 0.6+.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - CLI:
diffdock --version;boltz --version
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
ML Docking and Rescoring
Use machine-learning models for protein-ligand pose prediction and affinity scoring. Foundation models such as AlphaFold 3, Boltz, and Chai-1 handle protein-ligand complex prediction, while DiffDock-L extends the original DiffDock method for ligand-pose sampling (Corso et al. 2023, 2024). Boltz-2 reports affinity prediction approaching physics-based free-energy methods on its evaluated benchmarks at substantially lower computational cost. Physical plausibility remains a separate requirement: on the PoseBusters Benchmark, the original DiffDock produced a correct and physically valid pose for 12% of complexes, compared with 58% for Vina and 55% for GOLD (Buttenschoen et al. 2024). Use ML sampling with independent scoring and physical validation rather than treating model confidence as sufficient.
For classical docking, see chemoinformatics/virtual-screening. For pose validation (PoseBusters), see chemoinformatics/pose-validation. For free-energy calculations (post-docking), see chemoinformatics/free-energy-calculations. For PROTAC ternary complex prediction, see chemoinformatics/protac-degraders.
ML Docking Method Taxonomy
| Tool | Approach | Speed | Strength | Fails when |
|---|---|---|---|---|
| DiffDock-L (Corso et al. 2024) | Equivariant diffusion | GPU; hardware-dependent | Diverse pose sampling for cross-docking | Requires physical validation; OOD risk |
| Boltz-1 (Wohlwend et al. 2024) | AlphaFold-style foundation | GPU; hardware-dependent | Full complex prediction | Confidence is not affinity or physical validation |
| Boltz-2 (Passaro et al. 2025) | Boltz-1 + affinity module | GPU; hardware-dependent | Joint pose and affinity triage | Benchmark- and chemotype-dependent accuracy |
| Chai-1 (Chai Discovery 2024) | AlphaFold-style + language model | GPU; hardware-dependent | Open-weight complex prediction | Validate ligands and cofactors independently |
| AlphaFold 3 (Abramson et al. 2024) | Foundation model | Local code/weights or public server | Complex prediction with proteins and ligands | Server and local distributions have different terms and limits |
| EquiBind | Equivariant single-shot | <1s GPU | Fast pose | Lowest accuracy on PoseBusters |
| TANKBind | Distance + classifier | <1s GPU | Fast pose + score | Geometric inconsistency |
| NeuralPLexer | E3-equivariant generative model | GPU; hardware-dependent | Protein-ligand structure prediction | Validate geometry and confidence on the target domain |
| Glide (Schrödinger) | Grid-based docking and empirical scoring | License and hardware-dependent | Commercial docking workflow | License cost |
| GNINA 1.1 CNN | Classical sampling + CNN scoring | GPU; hardware-dependent | CNN-assisted pose ranking | Validate transfer to the target and chemotype |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 277 lines · 0 tokens per session scan A fa851992a4fc
bio-ml-docking-rescoring is a skill published in the GitHub repository GPTomics/bioSkills (1,226 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,151 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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