DeepCompoundNet: enhancing compound–protein interaction prediction with multimodal convolutional neural networks

Abstract

Virtual screening has emerged as a valuable computational tool for predicting compound–protein interactions, offering a cost-effective and rapid approach to identifying potential candidate drug molecules. Current machine learning-based methods rely on molecular structures and their relationship in the network. The former utilizes information such as amino acid sequences and chemical structures, while the latter leverages interaction network data, such as protein–protein interactions, drug–disease interactions, and protein–disease interactions. However, there has been limited exploration of integrating molecular information with interaction networks. This study presents DeepCompoundNet, a deep learning-based model that integrates protein features, drug properties, and diverse interaction data to predict chemical–protein interactions. DeepCompoundNet outperforms state-of-the-art methods for compound–protein interaction prediction, as demonstrated through performance evaluations. Our findings highlight the complementary nature of multiple interaction data, extending beyond amino acid sequence homology and chemical structure similarity. Moreover, our model’s analysis confirms that DeepCompoundNet gets higher performance in predicting interactions between proteins and chemicals not observed in the training samples. Communicated by Ramaswamy H. Sarma