FeatureDock for protein-ligand docking guided by physicochemical feature-based local environment learning using transformer

Abstract

Molecular docking, the task of predicting the binding structures between a protein and a small molecule ligand, plays a significant role in structural-based drug discovery. In recent years, numerous deep learning-based methods for molecular docking have emerged. State-of-the-art approaches such as DiffDock formulate the docking problem using diffusion generative models, exhibiting superior performance than traditional docking algorithms. However, despite the strong performance of these deep learning-based docking methods in predicting binding poses, they often lack a well-defined scoring function. This limitation poses challenges in effectively distinguishing between the strong and weak inhibitors during virtual screening. To address this limitation, we introduce FeatureDock, a transformer-based deep learning framework, which can leverage chemical features from protein local environments to accurately predict the protein-ligand binding poses as well as achieve a strong scoring power for virtual screening. We demonstrate the robustness of FeatureDock on Cyclin-Dependent Kinase 2 and Angiotensin-converting enzyme compound datasets.