MMGCSyn: Explainable synergistic drug combination prediction based on multimodal fusion

Abstract

Synergistic drug combinations are an effective solution for treating complex diseases. The main challenge is to improve the model performance of the unknown drug combination prediction task. Due to some drugs in the dataset being wholly excluded, it is difficult for the model to effectively extract the data features of these drugs, affecting the model’s accuracy and generalization ability. Unlike previous methods, we propose an interpretable synergistic drug combination prediction model, MMGCSyn, based on multimodal feature fusion. The process is as follows: First, given any (drug, drug, cell line) triple. For drug features, a graph attention network is used to extract drug molecular graph features, a deformable convolutional network is used to extract drug morgan fingerprint features and the spatial feature reconstruction module is used to suppress morgan fingerprint feature redundancy. Multi-layer MLP is used to extract the features of cell line features. Subsequently, feature fusion and prediction are performed through Transformer. We compared five existing methods on three drug combination datasets. The results show that MMGCSyn has achieved the best results and can effectively capture the chemical substructures of drug molecules.