Extrinsic-Intrinsic Representation Learning Framework for Drug Discovery

Tian Xia, Sarp Aykent, Wei-Shinn Ku

Abstract: Exploring drug-target interaction remains one of the essential tasks in drug discovery, and it is critical to gain a thorough understanding of the biological process and disease mechanisms. Despite recent successes in the application of machine learning approaches, drug-target interaction studies are still largely under-explored due to significant challenges in modeling different types of representations and capturing the inherent correlation between targets and drugs from low-level representations. What is more, the length of the target protein sequences and the complexity of the drug-target binding complex make the problem hard to handle. In this work, we focus on increasing the generalizability and interpretability of the drug-target prediction models and propose an Extrinsic-Intrinsic Representation learning model (EIR) intended to discover the inner correlation between target proteins and drugs on both the extrinsic and intrinsic levels. Our experimental results show that EIR makes more accurate predictions than the state-of-the-art method in both drug-target affinity prediction and drug-target interface prediction tasks and demonstrate the potential of the structural-free method for drug discovery.