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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Deep learning in retrosynthesis planning: datasets, models and tools.

Jingxin Dong1, Mingyi Zhao2, Yuansheng Liu1

  • 1College of Information Science and Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Hunan, China.

Briefings in Bioinformatics
|September 27, 2021
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) accelerates drug synthesis through deep learning-powered retrosynthetic analysis. This review details AI in retrosynthesis, covering datasets, models, and tools for researchers.

Keywords:
deep learninggraph neural networkretrosynthesisseq2seqtransformer

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Area of Science:

  • * Computational Chemistry
  • * Medicinal Chemistry
  • * Artificial Intelligence

Background:

  • * Drug synthesis is crucial for societal advancement.
  • * Retrosynthetic analysis is a key step in synthetic chemistry.
  • * Artificial intelligence (AI) is increasingly applied to chemical synthesis.

Purpose of the Study:

  • * To comprehensively review the development of retrosynthesis using deep learning.
  • * To cover datasets, models, and tools in AI-driven retrosynthesis.
  • * To provide insights into current challenges and future trends in the field.

Main Methods:

  • * Literature review of deep learning applications in retrosynthesis.
  • * Analysis of academic and industrial datasets, models, and platforms.
  • * Discussion of existing model limitations and future research directions.

Main Results:

  • * Identified key datasets and representative AI models for retrosynthesis.
  • * Detailed stable industry platforms for AI-powered retrosynthetic planning.
  • * Highlighted disadvantages of current AI models in retrosynthesis.

Conclusions:

  • * Deep learning significantly enhances retrosynthetic analysis for drug discovery.
  • * The review provides a foundational understanding for newcomers to AI in retrosynthesis.
  • * Future trends indicate continued advancements in AI-driven synthetic planning.