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Predicting Reaction Yields via Supervised Learning.

Andrzej M Żurański1, Jesus I Martinez Alvarado1, Benjamin J Shields1

  • 1Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States.

Accounts of Chemical Research
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Summary
This summary is machine-generated.

Machine learning (ML) models can predict organic reaction yields, aiding chemists in reaction design. DFT-derived features improve predictions, enabling mechanistic insights and experimental validation.

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Area of Science:

  • Computational Chemistry
  • Machine Learning in Organic Synthesis
  • Data-Driven Chemistry

Background:

  • Machine learning (ML) has revolutionized big data applications in various scientific disciplines.
  • Supervised ML offers potential for accurate chemical reactivity predictions in organic synthesis, assisting with reaction prediction, optimization, and mechanistic studies.
  • Representing chemical reactions with descriptive data, particularly DFT-derived physical features, is crucial for building effective ML models.

Purpose of the Study:

  • To review and provide perspective on three studies utilizing ML models for predicting reaction yield.
  • To evaluate the effectiveness of DFT-derived physical features versus random or naive features in ML models for reaction yield prediction.
  • To explore the application of ML in reaction development and mechanistic interrogation using both small and high-throughput experimentation (HTE) datasets.

Main Methods:

  • Development of supervised ML models using DFT-derived physical features of reacting molecules and conditions.
  • Application of linear regression on a small dataset (16 phosphine ligands in Ni-catalyzed Suzuki-Miyaura coupling).
  • Training and comparison of various ML algorithms on larger HTE datasets, contrasting DFT-based features with random and naive baseline models.

Main Results:

  • For small datasets, identifying single, reactivity-relevant features is critical.
  • DFT-based featurization provided moderate, but significant, out-of-sample prediction improvement for one of two HTE datasets.
  • The improvement was linked to specific features, leading to a testable mechanistic hypothesis that was experimentally validated.

Conclusions:

  • Supervised ML can enhance reaction yield predictions compared to simpler methods and aid in understanding reaction mechanisms.
  • DFT-based features can offer advantages in ML-driven reactivity modeling, particularly when linked to mechanistic insights.
  • Further research is needed to fully establish ML as an indispensable tool in reaction development and chemical reactivity modeling.