agentd-drug-discovery
The agentd-drug-discovery skill automates early-stage drug discovery by mining literature and databases for known ligands, generating molecular candidates through scaffold hopping and fragment growth, and ranking results using SAR and ADMET predictions. Use this skill when designing novel compounds against a target protein, exploring chemical space around reference molecules, or rapidly generating prioritized candidate lists constrained by drug-like properties.
git clone --depth 1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills /tmp/agentd-drug-discovery && cp -r /tmp/agentd-drug-discovery/skills/agentd-drug-discovery ~/.claude/skills/agentd-drug-discoverySKILL.md
<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu> # All Rights Reserved. # # This code is proprietary and confidential. # Unauthorized copying of this file, via any medium is strictly prohibited. # # Provenance: Authenticated by MD BABU MIA --> --- name: agentd-drug-discovery description: Use the AgentD workflow to mine evidence, design molecules, and rank candidates with SAR plus ADMET annotations for early drug discovery tasks. allowed-tools: - read_file - run_shell_command --- ## At-a-Glance - **description (10-20 chars):** Hypothesis foundry - **keywords:** ligand-design, SAR, ADMET, docking, ranking - **measurable_outcome:** Generate ≥10 candidate molecules (or requested count) with SMILES, key properties, and rationales per run, all delivered within 15 minutes. ## Inputs - `target_protein`, optional `reference_compound`, disease `indication`. - `constraints` dict (LogP, MW, TPSA, etc.) and `num_candidates`. ## Outputs 1. Ranked candidate list with SMILES + property scores + novelty metrics. 2. ADMET/toxicity alerts and SAR rationale per molecule. 3. Reproducibility manifest (data source versions, model checkpoints). ## Workflow 1. **Evidence retrieval:** Mine literature + databases for known ligands and liabilities. 2. **Generate candidates:** Run AgentD generative step (scaffold hopping/fragment growth) aligned to constraints. 3. **Score & filter:** Apply Lipinski/QED/ADMET heuristics; include docking setup when requested. 4. **Rank & explain:** Combine efficacy, developability, novelty; summarize SAR learnings. 5. **Deliver outputs:** Emit JSON/CSV plus narrative recommendations; mark as in silico. ## Guardrails - Clearly state outputs are hypothetical and need wet-lab validation. - Flag PAINS/reactive motifs automatically. - Record data/model versions for audit trails. ## References - Detailed parameter tables and dependencies listed in `README.md`. <!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
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