Multiscale Motif-Aware Relation Graph Structure for Drug-Target Binding Affinity Prediction

Exploring drug-target binding affinity (DTA) is essential for drug discovery. Numerous works rely on the one-dimensional SMILES representation of drugs for predicting drug-target affinity, but ignore the crucial structural information of drug molecules. Considering structural information is an important factor in determining the affinity properties of drugs, we propose using the multiscale motif-aware relation graph (MMRG) rather than the SMILES representation to build drug descriptors. MMRG explicitly provides crucial motif-level structural and topological information of drugs, thereby ameliorating the predictive power of models. With this idea, we propose a novel MMRG construction approach for drugs, including a multiscale motif-aware learning for extracting motif-level structural information from motifs with different sizes and a relation graph construction approach for extracting topological information from chemical bonds. We implement a graph convolutional network to learn from MMRGs, and the learned latent features are used to predict drug-target affinity. The experiment results on the Davis and KIBA dataset report that our model can significantly outperform existing methods in drug-target affinity prediction, with an average improvement of 15.61% and 8.50% respectively. We further explain the principle of MMRG to improve the drug-target affinity prediction accuracy by comparing its generative function with the state-of-the-art method.