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Diffusion-based generative AI for exploring transition states from 2D molecular graphs.

Seonghwan Kim1, Jeheon Woo1, Woo Youn Kim2,3

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Summary
This summary is machine-generated.

We developed TSDiff, a new machine learning model that predicts transition state (TS) geometries from 2D molecular graphs, improving accuracy and efficiency for chemical reaction exploration.

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Area of Science:

  • Computational chemistry
  • Machine learning in chemistry

Background:

  • Predicting transition state (TS) geometries is vital for understanding chemical reaction mechanisms and kinetics.
  • Current machine learning (ML) models for TS prediction require 3D reactant/product conformations, incurring significant computational costs and effort.

Purpose of the Study:

  • To introduce TSDiff, a novel generative approach for predicting TS geometries directly from 2D molecular graphs.
  • To demonstrate TSDiff's superior accuracy and efficiency compared to existing ML methods.

Main Methods:

  • Utilized a stochastic diffusion method for generative TS geometry prediction.
  • Trained the model on a diverse dataset of TS geometries for various reactions.
  • Input molecular data solely from 2D molecular graphs.

Main Results:

  • TSDiff achieved higher accuracy and efficiency than existing ML models that use 3D input geometries.
  • The model successfully samples diverse TS conformations, enabling exploration of multiple reaction pathways.
  • TSDiff identified reaction pathways with lower energy barriers than those in reference databases.

Conclusions:

  • TSDiff offers a computationally efficient and reliable method for exploring transition states.
  • This approach significantly reduces the prerequisites for TS geometry prediction, making it more accessible.
  • The ability to sample diverse TS conformations opens new avenues for discovering more favorable reaction pathways.