MPFFPSDC: A multi-pooling feature fusion model for predicting synergistic drug combinations

•This work proposes MPFFPSDC, a novel deep learning algorithm based on multi-pooling feature fusion for DDS prediction.•In MPFFPSDC, we introduce a new pooling method, which uses attention mechanism to aggregate atomic feature vectors into a drug feature vector.•MPFFPSDC can keep maintain the symmetry of drug inputs to eliminate the inconsistency of predictive results caused by different drug input sequences.•Compared with existing state-of-the-art methods, MPFFPSDC achieves higher predictive accuracy. Drug combination therapies are common practice in the treatment of cancer, but not all combinations result in synergy. As traditional screening approaches are restricted in their ability to uncover synergistic drug combinations, computer-aided medicine is becoming a increasingly prevalent in this field. In this work, a predictive model of potential interactions between drugs named MPFFPSDC is presented, which can maintain the symmetry of drug inputs and eliminate inconsistencies in predictive results caused by different drug inputting sequences or positions. The experimental results show that MPFFPSDC outperforms comparative models in major performance indicators and exhibits better generalization for independent data. Furthermore, the case study demonstrates that our model can capture molecular substructures that contribute to the synergistic effect of two drugs. These results indicate that MPFFPSDC not only offers strong predictive performance, but also has good model interpretability that may provide new insights for the study of drug interaction mechanisms and the development of new drugs.