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.