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The concept of prochirality leads to the nomenclature of the individual faces of a molecule and plays a crucial role in the enantioselective reaction. It is a concept where two or more achiral molecules react to produce chiral products. A typical process is the reaction of an achiral ketone to generate a chiral alcohol. Here, the achiral reactant reacts with an achiral reducing agent, sodium borohydride, to generate an equimolar mixture of the chiral enantiomers of the product. For example, an...
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Molecular machine learning (ML) models predict chemical reactions and design novel ligands for asymmetric catalysis. Experimental validation confirmed ML-generated reactions align with predictions, highlighting ML

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Area of Science:

  • Computational Chemistry
  • Machine Learning
  • Organic Synthesis

Background:

  • Machine learning (ML) is increasingly applied to chemical reaction prediction, but challenges include small, sparse, and skewed datasets.
  • Existing ML models excel at predicting known reactions; however, using deep generative models for novel reaction discovery and validation is less explored.
  • Catalytic asymmetric reactions, particularly β-C(sp3)-H activation, are crucial but complex, demanding efficient predictive and explorative tools.

Purpose of the Study:

  • To develop and validate deep learning models for predicting outcomes of catalytic asymmetric β-C(sp3)-H activation reactions.
  • To explore the use of generative deep learning models for designing novel chiral ligands to guide new reaction discovery.
  • To assess the reliability and novelty of ML-generated reaction predictions and ligand designs through experimental validation.

Main Methods:

  • A transfer learning approach was employed, utilizing a chemical language model (CLM) pretrained on a large molecule dataset.
  • The CLM was fine-tuned on a dataset of 220 experimentally reported asymmetric β-C(sp3)-H activation reactions.
  • An ensemble prediction (EnP) model, comprising 30 fine-tuned CLMs, was developed for predicting reaction enantioselectivity (% ee).
  • A separate CLM was fine-tuned on 77 known chiral ligands to generate novel ligand candidates.

Main Results:

  • The EnP model demonstrated high reliability in predicting the % ee for test set reactions.
  • The ligand-generating CLM produced novel ligands with high predicted validity.
  • Proof-of-concept wet-lab experiments validated most ML-generated reactions, showing excellent agreement with EnP predictions.

Conclusions:

  • Deep learning, through transfer learning and ensemble predictions, can effectively guide the discovery of new chemical reactions and ligands.
  • The study highlights the potential of ML in advancing ligand design for asymmetric catalysis.
  • Domain expertise remains crucial for critical decisions in ML-driven reaction development, balancing computational predictions with expert judgment.