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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Artificial intelligence in drug discovery: applications and techniques.

Jianyuan Deng1, Zhibo Yang2, Iwao Ojima3

  • 1Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA.

Briefings in Bioinformatics
|November 4, 2021
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Summary

Artificial intelligence (AI) is revolutionizing drug discovery by improving molecular property prediction and molecule generation. This survey details AI techniques, data resources, and applications, offering a comprehensive review for researchers.

Keywords:
artificial intelligencedrug discoverylearning paradigmmodel architecturemolecular property predictionmolecule generation

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Area of Science:

  • Computational chemistry and cheminformatics
  • Machine learning and artificial intelligence
  • Pharmaceutical sciences and drug development

Background:

  • Artificial intelligence (AI) has significantly impacted drug discovery over the last decade.
  • AI techniques are applied in critical areas like virtual screening and molecular design.
  • The field requires a structured overview of AI's role and methodologies.

Purpose of the Study:

  • To provide a comprehensive survey of AI applications in drug discovery.
  • To systematically review AI techniques, including model architectures and learning paradigms.
  • To organize the surveyed works chronologically to reflect technological advancements.

Main Methods:

  • Overview of drug discovery processes and key AI applications.
  • Categorization of AI applications into molecular property prediction and molecule generation.
  • Detailed analysis of AI model architectures and learning paradigms.
  • Presentation of common data resources, molecular representations, and benchmark platforms.

Main Results:

  • Identification of two primary AI tasks in drug discovery: molecular property prediction and molecule generation.
  • Chronological organization of AI techniques to illustrate historical development.
  • Compilation of relevant data resources, molecule representations, and benchmark platforms.
  • A curated GitHub repository with papers and code for ongoing learning.

Conclusions:

  • AI is a transformative force in modern drug discovery.
  • This survey offers a structured and chronological review of AI in the field.
  • The provided resources aim to facilitate further research and development in AI-driven drug discovery.