AI-Driven Integration of Transcriptomics, Quantum Mechanics, and Physiology for Predicting Drug-Induced Liver Injury in Data-Limited Scenarios

Drug-induced liver injury (DILI) is a significant concern with prescription medications and supplements. Accordingly, it is crucial to develop tools and approaches that can predict DILI likelihood of existing medications and supplements, as well as potential drug candidates under development. The complexity of liver injury mechanisms and the limited availability of DILI data hamper the development of robust predictive models. In order to overcome these challenges, this study investigated enriching machine learning/artificial intelligence (ML/AI) models that predict the risk of DILI using drug structural parameters along with rat liver transcriptomics data, quantum mechanics-derived features of the drug molecules, and metrics for interspecies variability of drug exposure. The enrichment of ML/AI models with such features dramatically improved ML/AI models’ DILI predictive ability, even in a severely data-limited scenario. The approach used in the study, especially the incorporation of knowledge-based features to enrich AI models, holds tremendous promise for not only assessing safety and toxicity assessments of drug candidates but also in other aspects such as target engagement and efficacy of these candidates, early in the development phase.药物性肝损伤（Drug-induced liver injury, DILI）是处方药与膳食补充剂领域备受关注的安全性问题。因此，开发能够预测现有处方药、补充剂以及在研潜在候选药物的DILI风险的工具与方法至关重要。但肝损伤机制的复杂性与DILI数据的匮乏，制约了鲁棒预测模型的研发。为破解上述难题，本研究针对药物性肝损伤风险预测开展了机器学习/人工智能（machine learning/artificial intelligence, ML/AI）模型的特征增强研究，具体纳入药物结构参数、大鼠肝脏转录组学数据、药物分子的量子力学衍生特征，以及药物暴露量的种间差异度量指标。即便在数据极度受限的场景下，通过此类特征增强优化后的ML/AI模型，其药物性肝损伤预测性能也得到了显著提升。本研究采用的方法，尤其是通过知识驱动特征富集优化人工智能模型的策略，不仅可为候选药物研发早期阶段的安全性与毒性评价提供新的可能，还可应用于候选药物的靶点结合情况、疗效等其他研发环节。