medchem

To filter a compound library for structural alerts and PAINS, apply medicinal chemistry rules and catalogs to your molecules to generate a passing or failing report. This process uses RDKit and the medchem package to triage drug-like candidates.

Yes, you can apply CNS filtering and complexity metrics to large compound libraries. The process supports scalable lead-optimization workflows by combining RuleFilters with optional parallel processing to triage drug-like candidates efficiently.

Medicinal chemistry rules available for lead optimization workflows include RuleFilters, NIBR alerts, Lilly alerts, PAINS catalogs, and complexity constraints. These rules combine to evaluate drug-likeness and filter molecules for structural alerts.

Library filtering handles structural alerts and complexity constraints by evaluating molecules against multiple medicinal chemistry catalogs and groups. It combines RuleFilters with NIBR and Lilly alerts to systematically identify and exclude failing compounds.

Yes, you need RDKit and pandas to filter compounds by drug-likeness rules. RDKit manages molecule handling while pandas structures the passing or failing report, and the medchem package supplies the necessary rules, structures, and complexity metrics.

The best way to triage drug-like candidates using medicinal chemistry rules is to run RuleFilters alongside structural-alert checks. This approach applies PAINS, NIBR, and Lilly catalogs to evaluate drug-likeness and complexity across your molecule list.