Predicting Renal Toxicity of Compounds with Deep Learning and Machine Learning Methods

Renal toxicity prediction plays a vital role in drug discovery and clinical practice, as it helps to identify potentially harmful compounds and mitigate adverse effects on the renal system. Compound with inherent renal-toxic potential is one of the major concerns for drug development as it leads to failure in drug discovery. Predicting nephrotoxic probabilities of a compound at an early stage can be effective for reducing the drug failure rate. Hence, it is crucial to develop a mechanism to analyze the renal toxicity of a drug-candidate optimally and quickly. To mitigate the risks associated with renal toxicity, predictive models leveraging machine learning and deep learning techniques have gained significant attention. In this study, 287 human renal-toxic drugs and 278 non-renal-toxic drugs were collected to develop a deep learning model and 27 machine learning models using 8 kinds of fingerprints and Rdkit descriptors. The deep neural network model shows better generalization scores on five-fold cross-validation and Extra-tree model shows better performance score on test data. Structural alerts, specific chemical substructures associated with renal toxicity, offer a valuable tool for early toxicity assessment. Therefore, the substructures of renal toxic compounds were studied by applying association rule mining technique based on frequent itemset patterns. A method has been proposed for generating structural alerts and 10 structural alerts have been generated using the method.