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Interpreting the Black Box: Interpretable Machine Learning and Systems Pharmacology in Small-Molecule Therapeutics
Huan Zhang1, Yangyang Wang2, Jihan Wang3
1Department of Joint Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an 710054, China.
Pharmaceutics
|June 26, 2026
Summary
Interpretable Machine Learning (IML) enhances drug development transparency by explaining AI decisions. This synergy with systems pharmacology aids in optimizing drug properties and predicting toxicity, accelerating precision medicine.
Area of Science:
- Pharmacology
- Artificial Intelligence
- Drug Development
Background:
- Small-molecule drug development suffers high attrition rates due to complex pharmacokinetics and toxicity.
- Deep learning models offer predictive power but lack mechanistic transparency, hindering clinical trust and regulatory approval.
Purpose of the Study:
- To review how Interpretable Machine Learning (IML), combined with systems pharmacology, enhances mechanistic transparency in drug development.
- To explore IML's role in optimizing drug properties, identifying toxicophores, guiding polypharmacology, and accelerating clinical translation.
Main Methods:
- Synthesizing literature on IML applications in drug development.
- Integrating multi-omics data with IML for rational polypharmacology.
- Discussing the use of IML for biomarker discovery and patient stratification.
Main Results:
- IML provides insights into algorithmic decisions, identifying features associated with Absorption, Distribution, Metabolism, and Excretion (ADME) optimization.
- IML aids in preemptively identifying toxicophores, though experimental validation is crucial for causality.
- IML facilitates rational polypharmacology and bridges in silico target identification with experimental validation.
Conclusions:
- IML integration with systems pharmacology offers a framework for transparent, evidence-based drug design.
- IML accelerates clinical translation through causal biomarker discovery and transparent regulatory documentation (Model Cards).
- Future challenges include data heterogeneity, out-of-distribution generalizability, and advancing Causal Artificial Intelligence for precision medicine.
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