Apply medicinal chemistry filters to chemical compounds for drug discovery.
npx skills add https://github.com/biomaps-infra/blender-opencode --skill bio-medchem
Please help me install this Agent Skill. Skill: bio-medchem Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/bio-medchem Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill bio-medchem
This Skill automates the complex and time-consuming process of filtering chemical compounds based on medicinal chemistry rules and structural alerts, enabling faster and more efficient drug discovery.
Use the bio-medchem skill to apply the Rule of Five and common structural alerts to a list of molecules.
High-intent search queries and answers about installing and using this skill.
Filtering chemical compounds for drug discovery involves applying medicinal chemistry rules like Lipinski's Rule of Five and structural alerts to assess drug-likeness. This process evaluates oral bioavailability and removes problematic substructures to prioritize candidates.
Structural alert filtering checks for problematic chemical substructures like PAINS, NIBR, and Lilly demerits. These alerts identify and remove molecules that can cause assay interference or toxicity during drug discovery.
Assess oral bioavailability by applying established drug-likeness rules like Lipinski's Rule of Five and Veber's rules. These rules evaluate molecular properties to determine oral bioavailability suitability for drug discovery candidates.
The medchem Python library supports PAINS filtering for compound libraries. It provides deterministic molecular analysis to apply structural alert filters and drug-likeness rules for drug discovery.
The best way to prioritize a large library of synthesized compounds is applying drug-likeness criteria and structural alert filters. This identifies candidates free from problematic chemical motifs for further testing.
inflexa-aiAutomate molecular structure analysis and QSAR modeling workflows with RDKit and DeepChem.
DrugClawProfile compound libraries and featurize molecules for pharmaceutical machine-learning workflows.
omar-A-hassanParse, standardize, and analyze molecular structures using datamol.
awslabsExecute RDKit pipelines for property calculation, library filtering, and similarity analysis.
