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Deep generative molecular design reshapes drug discovery.

Xiangxiang Zeng1, Fei Wang2, Yuan Luo3

  • 1College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, P.R. China.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) and deep generative models accelerate drug discovery. This review guides researchers on applying AI, covering methods, challenges, and future directions for computational drug development.

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Area of Science:

  • Medicinal Chemistry
  • Computational Biology
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) and deep generative models show significant promise in drug discovery and development.
  • Key challenges include selecting appropriate protocols, scrutinizing factors, and integrating diverse disciplines for AI application.

Purpose of the Study:

  • To provide an updated and accessible guide to AI and deep generative models for computational drug discovery.
  • To summarize classical and novel AI approaches relevant to medicinal applications.

Main Methods:

  • Review of classical and newly developed AI methodologies.
  • Introduction to deep generative models, including theoretical frameworks for chemical and biological structure representation.
  • Discussion of data and technical challenges in AI implementation.

Main Results:

  • AI and deep generative models offer powerful tools for various stages of drug discovery.
  • Understanding theoretical frameworks is crucial for representing and applying chemical and biological data.
  • Multimodal deep generative models represent a promising future direction.

Conclusions:

  • AI and deep generative models are essential for advancing drug discovery and development.
  • Addressing data and technical challenges is key to successful AI integration.
  • Future research should focus on multimodal models to further accelerate the process.