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Agentd Drug Discovery | Skills Pool

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Agentd Drug Discovery

Use the AgentD workflow to mine evidence, design molecules, and rank candidates with SAR plus ADMET annotations for early drug discovery tasks.

mdbabumiamssm24 스타2026. 2. 2.

직업
카테고리: 학술

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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

Ranked candidate list with SMILES + property scores + novelty metrics.
ADMET/toxicity alerts and SAR rationale per molecule.
Reproducibility manifest (data source versions, model checkpoints).

Workflow

Mine literature + databases for known ligands and liabilities.

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Agentd Drug Discovery

npx skills add mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-

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작성자: mdbabumiamssm
스타: 24
업데이트: 2026. 2. 2.
직업

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Evidence retrieval:

Generate candidates: Run AgentD generative step (scaffold hopping/fragment growth) aligned to constraints.

Score & filter: Apply Lipinski/QED/ADMET heuristics; include docking setup when requested.

Rank & explain: Combine efficacy, developability, novelty; summarize SAR learnings.

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.

02

Inputs

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