FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation

TL;DR

FABind+ advances molecular docking by significantly improving pocket prediction and pose generation, leading to faster, more accurate drug discovery processes with state-of-the-art performance.

Contribution

It introduces FABind+, an improved model that enhances pocket prediction and pose generation, building upon and surpassing the original FABind framework.

Findings

FABind+ outperforms the original FABind in accuracy.

The method achieves competitive state-of-the-art results.

Incorporates a simple sampling technique with a confidence model.

Abstract

Molecular docking is a pivotal process in drug discovery. While traditional techniques rely on extensive sampling and simulation governed by physical principles, these methods are often slow and costly. The advent of deep learning-based approaches has shown significant promise, offering increases in both accuracy and efficiency. Building upon the foundational work of FABind, a model designed with a focus on speed and accuracy, we present FABind+, an enhanced iteration that largely boosts the performance of its predecessor. We identify pocket prediction as a critical bottleneck in molecular docking and propose a novel methodology that significantly refines pocket prediction, thereby streamlining the docking process. Furthermore, we introduce modifications to the docking module to enhance its pose generation capabilities. In an effort to bridge the gap with conventional sampling/generative methods, we incorporate a simple yet effective sampling technique coupled with a confidence model, requiring only minor adjustments to the regression framework of FABind. Experimental results and analysis reveal that FABind+ remarkably outperforms the original FABind, achieves competitive state-of-the-art performance, and delivers insightful modeling strategies. This demonstrates FABind+ represents a substantial step forward in molecular docking and drug discovery. Our code is in https://github.com/QizhiPei/FABind.