Meditox: integrating machine learning and molecular data for accurate drug toxicity prediction

In the fields of chemical safety, regulatory toxicology, and drug development, toxicity prediction models are essential. The goal of this research is to use sophisticated machine learning and molecular descriptors, physicochemical characteristics, and structural fingerprints to create a dependable in silico model for predicting acute toxicity, hepatotoxicity, and mutagenicity. The model’s interpretability was enhanced through the use of feature selection approaches and a varied dataset comprising both private and public chemical structures. High accuracy and generalizability were validated by external testing and cross-validation. The model provided a scalable, economical toxicity screening method that outperformed current techniques. Deep learning and transfer learning will be used in future research to improve predictions.