molclaw-docking-screening

name	molclaw-docking-screening
description	High-level large-scale virtual screening workflow (10+ ligands) combining property filtering, QuickVina docking, EquiScore rescoring, and consensus ranking for target prioritization.
license	MIT license
metadata	{"skill-author":"PJLab"}

Large-Scale Docking Screening Skill

Note:

• Local files are not directly accessible by the server. Please upload them to the server using molclaw-file-transfer before execution.
• For PDB file inputs, it is recommended to preprocess them using molclaw-pdbfixer before execution.
• Please refer to skill molclaw-scp-server to complete tool invocation.

Name

molclaw-docking-screening

Description

This skill performs autonomous, large-scale virtual screening for a protein target using a soft pipeline:

1. Drug-likeness filtering (QED and Lipinski)
2. QuickVina2
3. EquiScore
4. Consensus ranking via rank aggregation

It is designed for 10+ molecules and should adapt strategy to input size, target quality, and tool outcomes.

Use this skill when:

• The task is virtual screening for 10+ ligands.
• The user asks for ranking, prioritization, or top-hit selection.
• You need balanced use of physics-based docking and ML rescoring.

Workflow Steps

Stage 0. Input Validation and Setup

• Validate SMILES list is non-empty and count >= 10 for this skill. If <10, still run but skip aggressive prefiltering.
• Determine run mode from task objective:
  • complete-ranking mode: user asks for all molecules ranked (common in MolBench-vs).
  • top-n mode: user asks for best N only.
• Resolve target structure:
  • If receptor_pdb_path exists, use it.
  • Else resolve target_chembl_id/uniprot_id and retrieve PDB.
• Optional chain extraction if chain is specified.
• Repair receptor with molclaw-pdbfixer (add hydrogens, remove waters/heterogens, normalize structure).
• Record all chosen settings in an execution summary for reproducibility.

Stage 1. Property Filtering (Adaptive)

• Compute QED and Lipinski violations for all candidates.
• Default filter: QED >= 0.2 and Lipinski violations <= 2.
• Soft adaptation by library size:
  • 10-50 molecules: keep default thresholds.
  • 51-200 molecules: consider stricter QED (e.g., 0.25-0.30) only if enough survivors remain.

  • 200 molecules: apply stronger triage and keep a broad but manageable subset for docking.

  • If survivors < max(top_n, 5), relax thresholds once and continue.

Stage 2. Pocket Identification

• If pocket_mode="provided", use given center and box.
• If pocket_mode="auto", predict pockets (P2Rank/fpocket) and choose the best-confidence pocket.
• Record selected pocket metadata (center, confidence, box dimensions).

Stage 3. QuickVina Docking

• Use skill molclaw-quickvina-docking workflow directly to obtain QuickVina ranking for the current candidate set.
• Do not re-implement its internal conversion/docking steps in this skill.
• Keep molecule ID/SMILES mapping for downstream consensus.
• Ensure the QuickVina workflow outputs a docked SDF file (or list of per-molecule SDF/PDBQT converted to SDF) that preserves receptor-relative 3D coordinates, so it can be directly passed to molclaw-equiscore-docking as docking_result_sdf_path.

Stage 4. EquiScore Rescoring

• Use skill molclaw-equiscore-docking workflow directly to obtain EquiScore ranking for the same candidate set.
• Do not re-implement its internal docking-input construction or conversion details in this skill.
• Prefer full-set rescoring in complete-ranking mode; keep output aligned by molecule ID/SMILES.
• If EquiScore tool returns prediction_count > 0 (and/or summary stats like max/min/mean/median), treat rescoring as successful; if predictions_path is not directly readable, fetch it via molclaw-file-transfer (server_file_to_base64) and continue.

Stage 5. Consensus Ranking

Do not directly add raw QuickVina and EquiScore scores.

1. Rank all successfully docked molecules by Vina affinity ascending (more negative is better): rank_vina.
2. Rank all available EquiScore predictions descending (larger is better): rank_equiscore.
3. For molecules with missing EquiScore, assign worst EquiScore rank plus penalty.
4. Compute fused rank sum:

• fused_rank = w1 * rank_vina + w2 * rank_equiscore

5. Sort fused_rank ascending (smaller is better).

Recommended default weights:

• w1 = 1.0
• w2 = 1.0

Notes

• Follow question-specific output requirements first.
• Keep score direction consistent: QuickVina lower is better, EquiScore higher is better.
• Use rank aggregation or z-score fusion; avoid directly summing raw scores.
• Some of steps depend on other skills, Please refer carefully.
• Save all intermediate scripts, and save the results (the complete Claude Code reasoning process and output for this question) as result.md.