As the size of accessible compound libraries expands to over 10 billion, the
need for more efficient structure-based virtual screening methods is emerging.
Different pre-screening methods have been developed to rapidly screen the
library while retaining the hit candidates, but the structure-based methods
applicable to general proteins are still lacking: the challenge is to predict
the binding pose between proteins and ligands and perform scoring in an
extremely short time. We introduce PharmacoNet, a deep learning framework that
identifies optimal 3D pharmacophore arrangements which a ligand should have for
stable binding from binding sites. By coarse-grained graph matching between
ligands and generated pharmacophore arrangements, we solve the expensive
binding pose sampling and scoring procedures of existing methods in a single
step. PharmacoNet is significantly faster than state-of-the-art structure-based
approaches, yet reasonably accurate even with a simple scoring function.
Furthermore, we show the promising result that PharmacoNet effectively retains
hit candidates even under the rigorous pre-screening threshold. Overall, our
study uncovers the hitherto untapped potential of a pharmacophore modeling
approach in deep learning-based drug discovery.

PharmacoNet 是一种深度学习框架，通过与生成的药效团配位体之间的粗粒化图形匹配，解决了现有方法中昂贵的配位体姿态采样和评分过程，从而显著提高了结构基准方法的速度，同时通过简单的评分函数保持了合理的准确性，并在严格的预筛选阈值下有效保留了命中候选物，揭示了药效团建模方法在基于深度学习的药物发现中迄今未被开发的潜力。