pharma-ml-tools

What problem does it solve?

This Skill streamlines the process of preparing and featurizing molecular data for machine learning in drug discovery, reducing manual effort and ensuring reproducibility.

Core Features & Use Cases

• Library Profiling: Standardize and analyze compound libraries for duplicates, invalid structures, and scaffold diversity using datamol.
• Molecular Featurization: Generate machine-learning-ready features (e.g., ECFP, MACCS, RDKit2D) using molfeat.
• Benchmark Dataset Fetching: Download and prepare standardized datasets for ADME, toxicity, DTI, and DDI studies using PyTDC.
• Medicinal Chemistry Screening: Apply common rules and alert filters (e.g., Rule of Five, lead-likeness) using medchem.
• Use Case: Prepare a library of drug candidates for QSAR modeling by profiling, featurizing, and screening them for potential liabilities.

Quick Start

Use the pharma-ml-tools skill to profile the compound library located at 'libraries/kinase_hits.csv' using the 'smiles' column and save the output to 'pharma_ml/kinase_hits_profile.csv'.