CWFBind: Geometry-Awareness for Fast and Accurate Protein-Ligand Docking

TL;DR

CWFBind is a novel protein-ligand docking method that incorporates local curvature features and degree-aware weighting to improve geometric accuracy and speed in binding conformation prediction.

Contribution

It introduces a geometry-aware approach using local curvature descriptors and weighting mechanisms, enhancing the accuracy and efficiency of deep learning-based docking.

Findings

Achieves competitive performance on docking benchmarks.

Improves pocket localization and binding conformation accuracy.

Balances speed and precision effectively.

Abstract

Accurately predicting the binding conformation of small-molecule ligands to protein targets is a critical step in rational drug design. Although recent deep learning-based docking surpasses traditional methods in speed and accuracy, many approaches rely on graph representations and language model-inspired encoders while neglecting critical geometric information, resulting in inaccurate pocket localization and unrealistic binding conformations. In this study, we introduce CWFBind, a weighted, fast, and accurate docking method based on local curvature features. Specifically, we integrate local curvature descriptors during the feature extraction phase to enrich the geometric representation of both proteins and ligands, complementing existing chemical, sequence, and structural features. Furthermore, we embed degree-aware weighting mechanisms into the message passing process, enhancing the model's ability to capture spatial structural distinctions and interaction strengths. To address the class imbalance challenge in pocket prediction, CWFBind employs a ligand-aware dynamic radius strategy alongside an enhanced loss function, facilitating more precise identification of binding regions and key residues. Comprehensive experimental evaluations demonstrate that CWFBind achieves competitive performance across multiple docking benchmarks, offering a balanced trade-off between accuracy and efficiency.