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AI-assisted synthesis prediction.

Simon Johansson1, Amol Thakkar2, Thierry Kogej3

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Artificial intelligence (AI) is revolutionizing chemical synthesis. This review covers AI for retrosynthesis, forward prediction, and quantum chemistry, alongside robotics for high-throughput experimentation to accelerate compound discovery.

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Area of Science:

  • Computational Chemistry
  • Organic Synthesis
  • Artificial Intelligence in Chemistry

Background:

  • Recent years have seen rapid advancements in applying AI technologies to chemical synthesis prediction.
  • AI/Machine Learning (ML) models are increasingly used to address complex synthesis-related challenges.

Purpose of the Study:

  • To provide a comprehensive summary of the latest advancements in AI for chemical synthesis.
  • To cover retrosynthesis planning, forward synthesis prediction, and quantum chemistry-based reaction prediction models.
  • To discuss the role of robotics and high-throughput experimentation in automated synthesis.

Main Methods:

  • Review of AI/ML models for synthesis prediction.
  • Analysis of reaction datasets used in model development.
  • Highlighting state-of-the-art robotics and high-throughput experimentation in the pharmaceutical industry.

Main Results:

  • AI models show significant progress in predicting chemical reactions.
  • Data sources for training AI models are crucial for their performance.
  • Robotics-based high-throughput experimentation is key for automated synthesis.

Conclusions:

  • AI and robotics are transforming chemical synthesis, enabling faster and more cost-effective compound creation.
  • The integration of predictive models and automated experimentation defines the future of synthetic chemistry.