Enhancing Bioactivity Prediction via Spatial Emptiness Representation of Protein-ligand Complex and Union of Multiple Pockets
Zhiyuan Zhou, Yueming Yin, Yiming Yang, Yuguang Mu, Hoi-Yeung Li, Adams Wai Kin Kong
Strong ligands can prevent endogenous substrates from binding to their target pockets, inhibiting or activating target proteins, making them promising drug candidates. The ligand's binding affinity determines the competitive binding outcome; stronger binding results in more effective drugs. Some ligands bind strongly to specific target pockets; however, occupancy and the corresponding target regions vary based on sizes and interactions. This variability may result in unoccupied pocket space, allowing endogenous substrates to compete with the ligand, which diminishes the binding interaction and reduces the ligand’s effectiveness as a drug candidate. Existing AI drug discovery methods highly concentrate on local information in the ligand or protein-ligand complex but neglect the unoccupied space, the spatial emptiness. To enhance bioactivity prediction, in this paper, we propose Geometric Representation of Spatial Emptiness in Protein-ligand Complex (GeoREC), which measures the empty space around each atom in the protein-ligand complex and Union of Multiple Pockets (Union-Pockets), which is composed of multiple pockets of the protein. They together provide a consistent and global view of the binding sites to drug discovery network. Furthermore, we note that the mean-square error (MSE) loss commonly employed in existing methods is not an ideal loss function for drug discovery because it considers each prediction independently. To alleviate this problem, we exploit a pairwise loss function. Extensive experiments on multiple datasets with diverse bioactivity types demonstrate that integrating the proposed representations and the loss function into state-of-the-art models leads to significant performance gains, highlighting their effectiveness.