OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs.

ABSTRACT:

Motivation

Deep graph learning (DGL) has been widely employed in the realm of ligand-based virtual screening. Within this field, a key hurdle is the existence of activity cliffs (ACs), where minor chemical alterations can lead to significant changes in bioactivity. In response, several DGL models have been developed to enhance ligand bioactivity prediction in the presence of ACs. Yet, there remains a largely unexplored opportunity within ACs for optimizing ligand bioactivity, making it an area ripe for further investigation.

Results

We present a novel approach to simultaneously predict and optimize ligand bioactivities through DGL and ACs (OLB-AC). OLB-AC possesses the capability to optimize ligand molecules located near ACs, providing a direct reference for optimizing ligan

SUBMITTER: Yin Y 

PROVIDER: S-EPMC11208724 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature