CAHNetF-DTP: A Community-Aware Heterogeneous Network-Based Embedding Framework for Drug-Target Interaction Prediction

Predicting possible drug-target interactions (DTIs) involves estimating the likelihood that drug molecules would bind to biological targets. By looking into the high cost and risk of in vivo and in vitro experiments, computational methods offer a cost- and time-efficient alternative for predicting DTIs in drug discovery. Many network-based methods have made significant progress in DTI prediction, but they often lack the ability to capture the topological and biological closeness between nodes, which is a crucial paradigm to identify hidden patterns in the DTI network. In this paper, we propose a novel computational-based framework─CAHNetF-DTP (Community Aware Heterogeneous Network-based Framework for Drug-Target interaction Prediction) that leverages a heterogeneous biomedical network comprising 15 distinct types of similarity-based subnetworks derived from drugs, targets, diseases, and side effects. To effectively capture the complex topological and semantic relationships among entities, we employ a community-aware framework that helps to capture local interaction patterns first before moving to another community. The resulting sequences are treated like sentences, enabling Word2Vec to generate embeddings that capture the contextual information on biomedical entities across multiple biological dimensions. To address the class imbalance issue, we incorporate a controlled negative sampling strategy and evaluate the proposed model under five experimental settings. The proposed method is further validated on benchmark data sets (KIBA and DAVIS), in real-world scenarios, and against SAR-based target prediction tools, where CAHNetF-DTP demonstrates robust and competitive performance, outperforming most of the methods. We carried out various case studies to check the model's capability to identify novel drug-target pairs. This work highlights the value of integrating heterogeneous biological data for improved DTI prediction.