MIDF-DMAP: Multimodal information dynamic fusion for drug molecule activity prediction

Abstract

BackgroundComputer-aided drug development can alleviate limitations in drug research and development processes, such as long cycles, high costs, and limited targeting. Deep learning models can be used to predict the activity of drug compound molecules. However, existing models still need to adequately address semantic redundancy, conflicts, and noise between different data modalities. It results in low accuracy in predicting molecular activity and many false positive results that need further resolution. ObjectiveThis study aims to design a parallel deep learning model for processing multimodal molecular information fusion, dynamically adjusting the weights of different modal features during the fusion process. This approach ensures that high-quality modal information is more significant in molecular activity prediction, improving the model’s predictive accuracy. MethodsFirst, three modal features of drug molecules are extracted: one-dimensional fingerprints, two-dimensional topological structures, and three-dimensional geometric structures. Then, a dynamic weighting mechanism is proposed to fuse these three modal features organically. Finally, the fused features are input into the model classifier to obtain molecular activity prediction results. ResultsExperimental results show that the proposed model improved the Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) value by 29.96% compared to the Weave model and 26.38% compared to the GC model on the BBBP dataset. Compared to the GraphMVP model and the multi-channel substructure graph model MSGG, the ROC-AUC value increased by 20.44% and 15.80%, respectively. This method also achieved good results on imbalanced samples and small molecule datasets. ConclusionThis study provides an effective multimodal dynamic fusion method for virtual drug screening, enriching computer-aided drug development theory and improving drug research and development efficiency.