Apply medicinal chemistry filters to prioritize drug-like compounds in molecule collections.
npx skills add https://github.com/swaruplab/operon --skill medchem-swaruplab
Please help me install this Agent Skill. Skill: medchem Source: https://github.com/swaruplab/operon/tree/main/src-tauri/protocols/medchem Command: npx skills add https://github.com/swaruplab/operon --skill medchem-swaruplab
Medicinal chemistry teams need scalable, rule-based curation of large compound libraries to quickly identify drug-like candidates.
Load a molecule list and run RuleFilters with desired rules to quickly identify drug-like candidates.
High-intent search queries and answers about installing and using this skill.
This skill applies medicinal chemistry filters to large molecule libraries by evaluating compounds against Rule of Five, Veber, CNS, PAINS, and structural alerts, returning structured results to prioritize drug-like candidates for high-throughput screening.
The PAINS filter identifies pan-assay interference compounds by applying structural alert rules to molecule collections. It flags problematic substructures that cause false positives during high-throughput screening, ensuring accurate lead discovery.
Yes, this skill leverages RDKit, pandas, and datamol to compute drug-likeness metrics and complexity scores for large molecule collections. It supports parallel processing to handle millions of molecules efficiently for downstream analysis.
Yes, this skill supports lead optimization by applying constraint checks, complexity metrics, and chemical group detection to compound libraries. It integrates with catalogs to prioritize leads and guide synthesis decisions.
The best way to triage libraries during high-throughput screening is to apply multiple medicinal chemistry rules like Ro5, Veber, and structural alerts simultaneously. This filters out non-drug-like molecules and prioritizes viable candidates.
Structural alerts flag chemically reactive or toxic substructures within compound collections. Applying these filters during lead discovery prevents downstream development failures and improves the quality of selected drug-like candidates.
tassiovaleTriage compound libraries with medicinal chemistry rules and structural alerts.
biomaps-infraQuery ChEMBL for bioactive molecules and drug discovery data via chembl_webresource_client.
robotlearning123Convert SMILES strings to molecule objects and compute molecular descriptors.
Lord1EgyptAccess standardized drug discovery datasets and molecular evaluation oracles via PyTDC.
