  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.
