Interpreting the Black Box: Interpretable Machine Learning and Systems Pharmacology in Small-Molecule Therapeutics

Small-molecule drug development faces high attrition rates driven by complex pharmacokinetics and unforeseen toxicities. While deep learning offers high predictive accuracy, its opaque "black-box" nature hinders mechanistic transparency, clinical trust, and regulatory approval. This review synthesizes how Interpretable Machine Learning, synergized with systems pharmacology, advances this paradigm by enhancing mechanistic transparency in drug development. By providing insights into algorithmic decisions, Interpretable Machine Learning helps researchers identify molecular features that are statistically associated with absorption, distribution, metabolism, and excretion optimization and preemptively mitigate toxicophores, while noting that these associations require experimental validation to establish genuine causality. Furthermore, integrating multi-omics data via Interpretable Machine Learning guides rational polypharmacology, bridging in silico target identification with "dry-wet loop" validations. Crucially, Interpretable Machine Learning accelerates clinical translation by discovering causal biomarkers, refining patient stratification, and generating transparent "Model Cards" to satisfy U.S. Food and Drug Administration/European Medicines Agency regulations. We also discuss future challenges: data heterogeneity, out-of-distribution generalizability, and the evolution toward Causal Artificial Intelligence. Ultimately, the integration of Interpretable Machine Learning provides a framework for more transparent and evidence-based drug design, realizing the promise of precision medicine.