Identifying Nephrotoxicity of Small Molecules Using Machine Learning

Nephrotoxicity is a severe condition characterized by kidney damage resulting from exposure to harmful substances such as drugs, diagnostic agents, chemicals, or environmental toxins. The potential for nephrotoxicity in drug molecules remains significant, often leading to severe consequences for patients. Despite existing computational methods for identifying nephrotoxic molecules, these approaches fail to provide stable and reliable performance due to biased modeling (e.g., small sample sizes, imbalanced classes, and data leakage). In this study, we offer a refined dataset for nephrotoxicity prediction tasks. Our dataset was collected from existing studies, rebalanced, and carefully curated to improve the quality of data for Quantitative Structure-Activity Relationship modeling. Additionally, we implemented a series of 32 prediction models using eight machine learning algorithms in combination with three types of molecular representations. The implementation of these machine learning models serves as a preliminary survey of how conventional methods perform on the refined dataset. Our findings indicated that all implemented models achieved satisfactory performance. Our dataset could serve as a valuable resource for developing more advanced prediction methods in the future.