Curated Bio-Activity & Molecular Validation Dataset for AI-Driven Chemical Biology Discovery Pipelines

This dataset provides a curated, quality-controlled collection of bio-activity and molecular validation parameters designed to support AI-driven drug discovery pipelines in chemical biology research. The dataset integrates: - Chemical valence and small molecule design filters - Protein stability and binding kinetics parameters - Biomolecule labeling and target engagement metrics - Safety, toxicity, and reactive group classifications - Standardized data annotation rubrics - Post-translational modification (PTM) validation entries Data was cross-referenced against global repositories including PDB, ChEMBL, and PubChem. Quality control parameters include automated structural valence auditing, Lipinski's Rule of Five compliance, PAINS screening, and binding energy thresholds. Keywords: Chemical Biology, Drug Discovery, AI/ML, Molecular Validation, ADMET, Target Engagement, Data Curation, Cheminformatics