ArticleNature communications2024
Diffusion-based generative AI for exploring transition states from 2D molecular graphs.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Automated transition state generation for mechanistic exploration in organic synthesis.Nature communications · 2026Article
- Flow matching for reaction pathway generation.Nature communications · 2026Article
- From SMILES Codes for Reactants and Products to Transition States With VeloxChem.Journal of computational chemistry · 2026Article
- A Comprehensive Dataset of Chemical Reactions Covering Second and Third Row Elements with Million-Scale Quantum Chemical Calculations.Scientific data · 2026Article
- ChemTorch: A Deep Learning Framework for Benchmarking and Developing Chemical Reaction Property Prediction Models.Journal of chemical information and modeling · 2026Article
- Rapid Generation of Transition-State Conformer Ensembles via Constrained Distance Geometry.Journal of chemical information and modeling · 2026Article
- Adaptive Transition-State Refinement with Learned Equilibrium Flows.Journal of chemical information and modeling · 2026Article
- GoFlow: efficient transition state geometry prediction with flow matching and E(3)-equivariant neural networks.Digital discovery · 2025Article
- Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states.Digital discovery · 2025Article
- AIQM2: organic reaction simulations beyond DFT.Chemical science · 2025Article
- Harnessing Machine Learning to Enhance Transition State Search with Interatomic Potentials and Generative Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Article
- A Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes.JACS Au · 2025Article
- Analytical ab initio hessian from a deep learning potential for transition state optimization.Nature communications · 2024Article
- 3DReact: Geometric Deep Learning for Chemical Reactions.Journal of chemical information and modeling · 2024Article
- A tabular data generation framework guided by downstream tasks optimization.Scientific reports · 2024Article
- Benchmarking machine-readable vectors of chemical reactions on computed activation barriers.Digital discovery · 2024Article
- Application and renovation evaluation of Dalian's industrial architectural heritage based on AHP and AIGC.PloS one · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics. Recently, machine learning (ML) models have shown remarkable performance for prediction of TS geometries. However, they require 3D conformations of reactants and products often with their appropriate orientations as input, which demands substantial efforts and computational cost. Here, we propose a generative approach based on the stochastic diffusion method, namely TSDiff, for prediction of TS geometries just from 2D molecular graphs. TSDiff outperforms the existing ML models with 3D geometries in terms of both accuracy and efficiency. Moreover, it enables to sample various TS conformations, because it learns the distribution of TS geometries for diverse reactions in training. Thus, TSDiff finds more favorable reaction pathways with lower barrier heights than those in the reference database. These results demonstrate that TSDiff shows promising potential for an efficient and reliable TS exploration.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.