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Related Experiment Video

Updated: Jul 28, 2025

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S

Yanfei Guan1, Taegyo Lee1, Ke Wang2

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|June 5, 2023
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Summary
This summary is machine-generated.

This study introduces a hybrid machine learning (ML) and transition state theory (TST) approach for predicting chemical reaction regioselectivity. The method accelerates predictions by using DFT calculations only when ML confidence is low.

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Area of Science:

  • Computational chemistry
  • Chemical reaction prediction
  • Machine learning in chemistry

Background:

  • Predicting regioselectivity is crucial for efficient pharmaceutical synthesis.
  • Current methods like DFT/TST and ML have limitations (time-consuming or data-dependent).

Purpose of the Study:

  • Develop a faster and accurate method for predicting reaction regioselectivity.
  • Integrate ML and DFT/TST to optimize computational resources.

Main Methods:

  • A hybrid workflow combining ML and DFT calculations.
  • DFT calculations are selectively triggered for low-confidence ML predictions.
  • The model was trained and tested on SNAr reaction datasets (Pfizer and USPTO).

Main Results:

  • Achieved 96.3% accuracy on the Pfizer dataset and 94.7% on the USPTO dataset.
  • Significantly reduced computational time compared to conventional TST methods.

Conclusions:

  • The hybrid ML-DFT approach offers a fast and accurate solution for regioselectivity prediction.
  • This method can streamline drug discovery by minimizing unproductive synthetic efforts.