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Small Molecule Drug Discovery Through Deep Learning:Progress, Challenges, and Opportunities by Li, Kun; Xiong, Yida; Zhang, Hongzhi; Cai, Xiantao; Du, Bo; Hu, Wenbin is a scholarly article available to read on EtoBox.

What is Small Molecule Drug Discovery Through Deep Learning:Progress, Challenges, and Opportunities about?

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 they 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 DL-based 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 offe

Author

Li, Kun; Xiong, Yida; Zhang, Hongzhi; Cai, Xiantao; Du, Bo; Hu, Wenbin

Published

2025

Language

EN