Structure-based drug discovery with deep learning

• URL: http://arxiv.org/abs/2212.13295v1
• Date: Mon, 26 Dec 2022 20:52:26 GMT
• Title: Structure-based drug discovery with deep learning
• Authors: R{\i}za \"Oz\c{c}elik, Derek van Tilborg, Jos\'e Jim\'enez-Luna, Francesca Grisoni
• Abstract summary: Artificial intelligence (AI) in the form of deep learning bears promise for drug discovery and chemical biology. This review summarizes the most prominent algorithmic concepts in structure-based deep learning for drug discovery.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence (AI) in the form of deep learning bears promise for drug discovery and chemical biology, $\textit{e.g.}$, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules $\textit{de novo}$. While most of the deep learning efforts in drug discovery have focused on ligand-based approaches, structure-based drug discovery has the potential to tackle unsolved challenges, such as affinity prediction for unexplored protein targets, binding-mechanism elucidation, and the rationalization of related chemical kinetic properties. Advances in deep learning methodologies and the availability of accurate predictions for protein tertiary structure advocate for a $\textit{renaissance}$ in structure-based approaches for drug discovery guided by AI. This review summarizes the most prominent algorithmic concepts in structure-based deep learning for drug discovery, and forecasts opportunities, applications, and challenges ahead.

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