In silico prediction of drug-induced rhabdomyolysis with machine-learning models and structural alerts

Abstract

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.

Keywords: drug-induced rhabdomyolysis; machine learning; molecular fingerprints; physicochemical properties; structural alerts.