Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China.
J Appl Toxicol. 2019 Aug;39(8):1224-1232. doi: 10.1002/jat.3808. Epub 2019 Apr 21.
Drug-induced rhabdomyolysis (DIR) is a serious adverse reaction and can be fatal. In the present study, we focused on the modeling and understanding of the molecular basis of DIR of small molecule drugs. A series of machine-learning models were developed using an Online Chemical Modeling Environment platform with a diverse dataset. A total of 80 machine-learning models were generated. Based on the top-performing individual models, a consensus model was also developed. The consensus model was available at https://ochem.eu/model/32214665, and the individual models can be accessed with the corresponding model IDs on the website. Furthermore, we also analyzed the difference of distributions of eight key physicochemical properties between rhabdomyolysis-inducing drugs and non-rhabdomyolysis-inducing drugs. Finally, structural alerts responsible for DIR were identified from fragments of the Klekota-Roth fingerprints.
药物诱导的横纹肌溶解症（DIR）是一种严重的不良反应，可能致命。在本研究中，我们专注于小分子药物 DIR 的建模和分子基础的理解。使用具有多样化数据集的在线化学建模环境平台开发了一系列机器学习模型。共生成了 80 个机器学习模型。基于表现最佳的个体模型，还开发了共识模型。共识模型可在 https://ochem.eu/model/32214665 获得，个体模型可在网站上使用相应的模型 ID 访问。此外，我们还分析了 8 种关键物理化学性质在诱导横纹肌溶解症的药物和非诱导横纹肌溶解症的药物之间的分布差异。最后，从 Klekota-Roth 指纹的片段中确定了导致 DIR 的结构警示。
