PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling
This addresses the need for rapid, structure-based virtual screening applicable to various proteins in drug discovery, representing a novel application rather than an incremental improvement.
The paper tackles the challenge of efficiently screening large compound libraries (over 10 billion compounds) for drug discovery by introducing PharmacoNet, a deep-learning framework for structure-based pharmacophore modeling, which is significantly faster than state-of-the-art methods while retaining hit candidates effectively under high filtration rates.
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 for rapid screening, but there is still a lack of structure-based methods applicable to various proteins that perform protein-ligand binding conformation prediction and scoring in an extremely short time. Here, we describe for the first time a deep-learning framework for structure-based pharmacophore modeling to address this challenge. We frame pharmacophore modeling as an instance segmentation problem to determine each protein hotspot and the location of corresponding pharmacophores, and protein-ligand binding pose prediction as a graph-matching problem. PharmacoNet is significantly faster than state-of-the-art structure-based approaches, yet reasonably accurate with a simple scoring function. Furthermore, we show the promising result that PharmacoNet effectively retains hit candidates even under the high pre-screening filtration rates. Overall, our study uncovers the hitherto untapped potential of a pharmacophore modeling approach in deep learning-based drug discovery.