AMPred-MFG: Investigating the Mutagenicity of Compounds Using Motif-Based Graph Combined with Molecular Fingerprints and Graph Attention Mechanism.

Accurate mutagenicity prediction is crucial in drug discovery, but evaluating the mutagenicity of drug molecules is technically challenging, and experimental mutagenicity tests are both time-consuming and costly. In this study, we introduce a new deep learning predictive model called AMPred-MFG, designed to predict the mutagenicity of drug molecules. Our approach first combines multiple molecular fingerprint features with molecular graph features to obtain a comprehensive molecular representation. Next, considering the significance of molecular substructures on mutagenicity, we decompose the molecules into motifs to form motif-based graph. We use a graph transformer to extract features from these motif-based graphs and fuse motif graph features with molecular fingerprint features and molecular graph features to create the final molecular representation. Finally, we use a multilayer perceptron to classify the compounds into mutagens and non-mutagens. We evaluate the performance of AMPred-MFG through ten-fold cross-validation experiments and validate its robustness on external validation datasets. By comparing with several state-of-the-art algorithms, AMPred-MFG achieves the best results in terms of performance, with AUC value of 0.912, ACC of 0.835, SEN of 0.849, NPV of 0.811, PPV of 0.854, MCC of 0.665. In addition, we use attention scores to identify molecular fragments related to mutagenicity, highlighting the interpretability of AMPred-MFG. We believe that AMPred-MFG can act as a dependable tool for predicting mutagenicity, allowing for the assessment of both mutagens and non-mutagens during the early phases of drug development. AMPred-MFG is freely available at https://github.com/zhaoqi106/AMPred-MFG .