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Deep Generative Models in Drug Molecule Generation. | LitMetric

Deep Generative Models in Drug Molecule Generation.

J Chem Inf Model

School of Software, Shandong University, Jinan 250100, China.

Published: April 2024

AI Article Synopsis

  • New drug discovery methods are crucial for improving human health, but traditional approaches are often time-consuming and costly.
  • Recent advancements in artificial intelligence, particularly deep generative models, show great promise in efficiently generating drug-like molecules with desirable properties.
  • This study reviews progress in molecule generation, highlights current challenges in the field, and suggests future research directions to enhance drug discovery efforts.

Article Abstract

The discovery of new drugs has important implications for human health. Traditional methods for drug discovery rely on experiments to optimize the structure of lead molecules, which are time-consuming and high-cost. Recently, artificial intelligence has exhibited promising and efficient performance for drug-like molecule generation. In particular, deep generative models achieve great success in generation of drug-like molecules with desired properties, showing massive potential for novel drug discovery. In this study, we review the recent progress of molecule generation using deep generative models, mainly focusing on molecule representations, public databases, data processing tools, and advanced artificial intelligence based molecule generation frameworks. In particular, we present a comprehensive comparison of state-of-the-art deep generative models for molecule generation and a summary of commonly used molecular design strategies. We identify research gaps and challenges of molecule generation such as the need for better databases, missing 3D information in molecular representation, and the lack of high-precision evaluation metrics. We suggest future directions for molecular generation and drug discovery.

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Source
http://dx.doi.org/10.1021/acs.jcim.3c01496DOI Listing

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