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

arXiv:2403.20261v1 Announce Type: cross
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 …

arxiv cs.ai cs.lg molecular docking prediction q-bio.bm through type