A Chemically Interpretable and Leakage‐Safe Machine Learning Framework for Predicting Molecular Refractive Index ( <i>n</i> _D )

ABSTRACT The molecular refractive index () governs light–matter interactions and underpins optical biosensors, bioimaging agents, lab‐on‐a‐chip devices, and functional biomaterials. Existing machine‐learning models for emphasize predictive accuracy but provide limited chemical interpretability and safeguards against information leakage. Here, we present an interpretable, leakage‐safe framework for predicting from structure‐derived descriptors and fingerprints. A curated dataset is processed via multi‐toolkit feature generation, removal of low‐information channels, diversity‐aware filtering, and ANOVA F‐test feature selection within a pipeline that performs median imputation and standardization. Sparse and generalized linear models enable attribution of variation to chemically meaningful descriptors. The final Lasso model shows strong generalization, with a mean fold‐specific test performance of (MAE ), the highest fold‐specific test performance of , and a definitive held‐out unseen test‐set performance of (MAE ) after retraining on the complete training set. Ablation studies identify feature selection, pipeline‐based preprocessing, and targeted tuning as the primary contributors to predictive performance in the selected framework.