Predicts ADMET properties for batches of molecules from SMILES strings or files.
npx skills add https://github.com/InternScience/MolClaw --skill molclaw-admet
Please help me install this Agent Skill. Skill: molclaw-admet Source: https://github.com/InternScience/MolClaw/tree/main/skills/L1_tools/molclaw-admet Command: npx skills add https://github.com/InternScience/MolClaw --skill molclaw-admet
Predict the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of input molecules from SMILES lists or files, accelerating early-stage drug discovery.
Provide a SMILES list or a path to a SMILES file to start predictions and receive structured ADMET results.
High-intent search queries and answers about installing and using this skill.
To predict ADMET properties for molecules, provide a SMILES list or SMILES file path. The tool returns per-molecule pharmacokinetics predictions and a JSON file path for export, enabling scalable screening during drug discovery.
ADMET prediction evaluates absorption, distribution, metabolism, excretion, and toxicity profiles of input molecules from SMILES strings. It filters unfavorable compounds during lead optimization to prioritize promising drug candidates.
Yes, batch ADMET predictions from SMILES files are supported. The tool processes multiple molecules simultaneously, returning structured JSON results with per-molecule predictions and a json_file path for export.
Yes, ADMET prediction is designed for early-stage drug discovery workflows. It enables scalable screening and prioritization of compounds by rapidly filtering molecules with unfavorable pharmacokinetic profiles during lead optimization.
ADMET prediction results are returned as a structured output including status, message, and json_content. This contains per-molecule predictions alongside a json_file path for exporting the complete drug discovery screening data.
ADMET screening during lead optimization rapidly filters out compounds with unfavorable properties. By evaluating toxicity and pharmacokinetics from SMILES inputs, it focuses resources on promising candidates for drug design.
InternScienceConvert IUPAC chemical names to SMILES strings via OPSIN.
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