Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques - Delre, Pietro; Lavado, Giovanna J.; Lamanna, Giuseppe; Saviano, Michele; Roncaglioni, Alessandra; Benfenati, Emilio; Mangiatordi, Giuseppe Felice; Gadaleta, Domenico (Frontiers in Pharmacology, 2022)
Drug-induced cardiotoxicity is a common side effect of drugs in clinical use or under post-market surveillance and is commonly due to off-target interactions with the cardiac Human-Ether-a-go-go-Related (hERG) potassium channel. Therefore, prioritizing drug candidates based on their hERG blocking potential is a mandatory step at the early preclinical stage of a drug discovery program. Herein, we trained and properly validated 30 ligand-based classifiers of hERG-related cardiotoxicity based on 7,963 curated compounds extracted by the freely accessible repository ChEMBL (version 25). Different machine learning algorithms were tested, namely random forest, K-nearest neighbours, gradient boosting, extreme gradient boosting, multilayer perceptron, and support vector machine. The application of: i) the best practises for data curation; ii) the feature selection method VSURF and iii) the Synthetic Minority Oversampling Technique (SMOTE) to properly handle the unbalanced data, allowed developing highly predictive models (BAMAX = 0.91, AUCMAX = 0.95). Remarkably, the undertaken temporal validation approach not only supported the predictivity of the herein presented classifiers but also suggested their ability to outperform those models commonly used in the literature. From a more methodological point of view, the paper put forward a new computational workflow, freely available in the GitHub repository (https://github.com/PDelre93/hERG-QSAR), as valuable for building highly predictive models of hERG-mediated cardiotoxicity.