AMES/QSAR predictions based on the combination of molecular graphs and molecular descriptors

Abstract

The 2nd AMES/QSAR International Challenge Project is held for the newly established Ames- test database for approximately 13,000 compounds. This study aims to provide a new predictive benchmark for this database using statistical methods (QSAR). We have developed an AMES/QSAR model using Graph Neural Network, which extracts features from molecular graphs through End-to-End learning and machine learning models based on molecular descriptors (LightGBM, XGBoost, and Neural Network). Our modeling scheme introduced the stacking ensemble method to integrate the predictions of each model. This is motivated by the ability to combine the different input representations of molecular structures and different classifiers' algorithms with improving the prediction accuracy. Our models showed good prediction performance for machine learning methods based on molecular descriptors and Graph Neural Network. The Stacking models of these models show further improvement in prediction accuracy. This study's findings can be used as a benchmark for AMES/QSAR models for new mutagenicity databases.