Development of a robust Machine learning model for Ames test outcome prediction

The mutagenicity is an essential parameter for evaluating the safety of pharmaceuticals, chemicals, consumer products, environmentally related compounds and the Ames assay is a significant test for predicting the mutagenicity of chemical compounds. In the data-driven era, developing robust models for efficient mutagenicity prediction before synthesizing and testing in vitro has gained increasing attention. In this study, a machine learning model that could predict Ames mutagenicity based on 2D molecular descriptors was developed. A multistep filtering process that adequately helps in identifying significant descriptors was adopted in this study. Three different sets of descriptors, namely, RDKit, Mordred and CDK were used to train three machine learning algorithms, viz., random forest, xgboost and catboost. The datasets were collected from different resources to develop a robust machine learning model. The robustness of this model was further validated by comparing different available ML and DL models for Ames genotoxicity. Specifically, 12 models, including our xgboost model, were used to validate an external dataset, and our model exhibited excellent performance, with an impressive AUC of 0.97. The codes to predict the genotoxicity of a molecule is available at https://github.com/Naga270588/Genotoxicity. © 2024 Elsevier B.V.