SphereDiff-TS: Sphere Space Diffusion Modeling for Accurate 3D Transition State Geometry Prediction.
Chong Zhao1,2, Pan Li1,2, Shu Zhang1,2
1State Key Laboratory of Discovery and Utilization of Functional Components in Traditional Chinese Medicine, Guizhou Medical University, Guiyang, Guizhou 550025, P. R. China.
ACS Omega
|May 11, 2026
Summary
SphereDiff-TS is a new diffusion model that accurately predicts 3D transition states (TS) for chemical reactions. This method overcomes computational costs and improves physical constraint integration for reaction mechanism exploration.
Area of Science:
- Computational Chemistry
- Machine Learning
- Quantum Chemistry
Background:
- Conventional quantum chemistry methods are computationally expensive.
- Existing machine learning models struggle to incorporate physical constraints effectively.
Purpose of the Study:
- To develop a diffusion-based method for predicting 3D transition state (TS) structures.
- To address the limitations of computational cost and physical constraint incorporation in current models.
Main Methods:
- Introduced SphereDiff-TS, a diffusion model utilizing a spherical coordinate system.
- Incorporated flexible boundary and dynamic radius constraints.
- Evaluated model performance against true transition states.
Main Results:
- Achieved chemical accuracy in geometry prediction (median RMSD: 0.048 Å).
- Demonstrated high accuracy in energy prediction (median absolute error: 0.55 kcal/mol).
- Accurately reproduced reaction barrier heights with deviations below 1.5 kcal/mol.
Conclusions:
- SphereDiff-TS shows potential as a robust computational tool.
- The method can aid in exploring chemical reaction mechanisms.
- It supports computer-driven reaction design.
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