Genomics and Computational Biology (GCB) Graduate Program, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
CPT Pharmacometrics Syst Pharmacol. 2023 Aug;12(8):1072-1079. doi: 10.1002/psp4.12975. Epub 2023 Jul 20.
In computational toxicology, prediction of complex endpoints has always been challenging, as they often involve multiple distinct mechanisms. State-of-the-art models are either limited by low accuracy, or lack of interpretability due to their black-box nature. Here, we introduce AIDTox, an interpretable deep learning model which incorporates curated knowledge of chemical-gene connections, gene-pathway annotations, and pathway hierarchy. AIDTox accurately predicts cytotoxicity outcomes in HepG2 and HEK293 cells. It also provides comprehensive explanations of cytotoxicity covering multiple aspects of drug activity, including target interaction, metabolism, and elimination. In summary, AIDTox provides a computational framework for unveiling cellular mechanisms for complex toxicity endpoints.
在计算毒理学中，复杂终点的预测一直具有挑战性，因为它们通常涉及多个不同的机制。最先进的模型要么由于准确性低而受到限制，要么由于其黑盒性质而缺乏可解释性。在这里，我们引入了 AIDTox，这是一种可解释的深度学习模型，它包含了对化学-基因联系、基因途径注释和途径层次结构的精心整理的知识。AIDTox 准确地预测了 HepG2 和 HEK293 细胞的细胞毒性结果。它还提供了对细胞毒性的全面解释，涵盖了药物活性的多个方面，包括靶标相互作用、代谢和消除。总之，AIDTox 为揭示复杂毒性终点的细胞机制提供了一个计算框架。
