PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling

• URL: http://arxiv.org/abs/2310.00681v3
• Date: Mon, 18 Dec 2023 06:03:08 GMT
• Title: PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling
• Authors: Seonghwan Seo and Woo Youn Kim
• Abstract summary: We describe for the first time a deep-learning framework for structure-based pharmacophore modeling to address this challenge. PharmacoNet is significantly faster than state-of-the-art structure-based approaches, yet reasonably accurate with a simple scoring function.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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