Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities
Journal:
arXiv
Published Date:
Feb 13, 2025
Abstract
Due to their excellent drug-like and pharmacokinetic properties, small
molecule drugs are widely used to treat various diseases, making them a
critical component of drug discovery. In recent years, with the rapid
development of deep learning (DL) techniques, DL-based small molecule drug
discovery methods have achieved excellent performance in prediction accuracy,
speed, and complex molecular relationship modeling compared to traditional
machine learning approaches. These advancements enhance drug screening
efficiency and optimization and provide more precise and effective solutions
for various drug discovery tasks. Contributing to this field's development,
this paper aims to systematically summarize and generalize the recent key tasks
and representative techniques in graph-structured small molecule drug discovery
in recent years. Specifically, we provide an overview of the major tasks in
small molecule drug discovery and their interrelationships. Next, we analyze
the six core tasks, summarizing the related methods, commonly used datasets,
and technological development trends. Finally, we discuss key challenges, such
as interpretability and out-of-distribution generalization, and offer our
insights into future research directions for small molecule drug discovery.
