Recent Deep Learning Applications to Structure-Based Drug Design

Methods Mol Biol. 2024:2714:215-234. doi: 10.1007/978-1-0716-3441-7_13.


Identification and optimization of small molecules that bind to and modulate protein function is a crucial step in the early stages of drug development. For decades, this process has benefitted greatly from the use of computational models that can provide insights into molecular binding affinity and optimization. Over the past several years, various types of deep learning models have shown great potential in improving and enhancing the performance of traditional computational methods. In this chapter, we provide an overview of recent deep learning-based developments with applications in drug discovery. We classify these methods into four subcategories dependent on the task each method is aiming to solve. For each subcategory, we provide the general framework of the approach and discuss individual methods.

Keywords: Ab initio molecule generation; Computational molecule representation; Computer-aided drug design; Deep learning; Generative-adversarial networks (GANs); Lead optimization; Ligand pose generation; Ligand pose scoring; Pharmacokinetic optimization; Structure-based drug design.

Publication types

  • Review
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, N.I.H., Extramural

MeSH terms

  • Deep Learning*
  • Drug Design
  • Drug Development
  • Drug Discovery