Title: Rigorous Discovery of Structure-Activity Patterns in Synthetic Drug Candidates via Regularized Regression and Chemical Rule Integration

Description: Apply robust, regularized regression modeling directly to the recorded molecular descriptors and activity scores in synthetic_drug_dev_portfolio.csv to systematically uncover descriptor combinations most predictive of compound activity. Conduct systematic feature selection with cross-validated performance metrics, ensuring that newly identified associations are both statistically significant and reproducible within the dataset's bounds. Integrate established cheminformatics rules to filter and validate discovered patterns for chemical plausibility, preventing the identification of spurious or non-physical correlations. Perform post-hoc clustering of high-activity compounds to contextualize descriptor patterns within distinct chemical property regimes present in the synthetic dataset. Report only those descriptor-activity relationships confirmed as physically interpretable and statistically robust using exclusively the available recorded data. This method advances data-driven drug discovery by tightly coupling statistical rigor with chemical validity in synthetic candidate evaluation.