Improving binding affinity prediction by emphasizing local features of drug and protein

Binding affinity prediction has been considered as a fundamental task in drug discovery. Despite much effort to improve accuracy of binding affinity prediction, the prior work considered only macro-level features that can represent the characteristics of the whole architecture of a drug and a target protein, and the features from local structure of the drug and the protein tend to be lost. In this paper, we propose a deep learning model that can comprehensively extract the local features of both a drug and a target protein for accurate binding affinity prediction. The proposed model consists of two components named as Multi-Stream CNN and Multi-Stream GCN, each of which is responsible for capturing micro-level characteristics or local features from subsequences of a target protein sequence and subgraph of a drug molecule, respectively. Having multiple streams consisting of different numbers of layers, both the components can compute and preserve the local features with a stream consisting of a single layer. Our evaluation with two popular datasets, Davis and KIBA, demonstrates that the proposed model outperforms all the baseline models using the global features, implying that local features play significant roles of binding affinity prediction.