React-OT: Optimal Transport for Generating Transition State in Chemical Reactions
- URL: http://arxiv.org/abs/2404.13430v2
- Date: Tue, 15 Oct 2024 22:05:54 GMT
- Title: React-OT: Optimal Transport for Generating Transition State in Chemical Reactions
- Authors: Chenru Duan, Guan-Horng Liu, Yuanqi Du, Tianrong Chen, Qiyuan Zhao, Haojun Jia, Carla P. Gomes, Evangelos A. Theodorou, Heather J. Kulik,
- Abstract summary: We develop React-OT, an optimal transport approach for generating unique Transition State structures from reactants and products.
Re React-OT generates highly accurate TS structures with a median structural root mean square deviation (RMSD) of 0.053AA and median barrier height error of 1.06 kcal/mol requiring only 0.4 second per reaction.
- Score: 45.99250641377074
- License:
- Abstract: Transition states (TSs) are transient structures that are key in understanding reaction mechanisms and designing catalysts but challenging to be captured in experiments. Alternatively, many optimization algorithms have been developed to search for TSs computationally. Yet the cost of these algorithms driven by quantum chemistry methods (usually density functional theory) is still high, posing challenges for their applications in building large reaction networks for reaction exploration. Here we developed React-OT, an optimal transport approach for generating unique TS structures from reactants and products. React-OT generates highly accurate TS structures with a median structural root mean square deviation (RMSD) of 0.053{\AA} and median barrier height error of 1.06 kcal/mol requiring only 0.4 second per reaction. The RMSD and barrier height error is further improved by roughly 25\% through pretraining React-OT on a large reaction dataset obtained with a lower level of theory, GFN2-xTB. We envision that the remarkable accuracy and rapid inference of React-OT will be highly useful when integrated with the current high-throughput TS search workflow. This integration will facilitate the exploration of chemical reactions with unknown mechanisms.
Related papers
- Text-Augmented Multimodal LLMs for Chemical Reaction Condition Recommendation [50.639325453203504]
MM-RCR is a text-augmented multimodal LLM that learns a unified reaction representation from SMILES, reaction graphs, and textual corpus for chemical reaction recommendation (RCR)
Our results demonstrate that MM-RCR achieves state-of-the-art performance on two open benchmark datasets.
arXiv Detail & Related papers (2024-07-21T12:27:26Z) - ReactAIvate: A Deep Learning Approach to Predicting Reaction Mechanisms and Unmasking Reactivity Hotspots [4.362338454684645]
We develop an interpretable attention-based GNN that achieved near-unity and 96% accuracy for reaction step classification.
Our model adeptly identifies key atom(s) even from out-of-distribution classes.
This generalizabilty allows for the inclusion of new reaction types in a modular fashion, thus will be of value to experts for understanding the reactivity of new molecules.
arXiv Detail & Related papers (2024-07-14T05:53:18Z) - ReactXT: Understanding Molecular "Reaction-ship" via Reaction-Contextualized Molecule-Text Pretraining [76.51346919370005]
We propose ReactXT for reaction-text modeling and OpenExp for experimental procedure prediction.
ReactXT features three types of input contexts to incrementally pretrain LMs.
Our code is available at https://github.com/syr-cn/ReactXT.
arXiv Detail & Related papers (2024-05-23T06:55:59Z) - Retrosynthesis prediction enhanced by in-silico reaction data
augmentation [66.5643280109899]
We present RetroWISE, a framework that employs a base model inferred from real paired data to perform in-silico reaction generation and augmentation.
On three benchmark datasets, RetroWISE achieves the best overall performance against state-of-the-art models.
arXiv Detail & Related papers (2024-01-31T07:40:37Z) - Accurate transition state generation with an object-aware equivariant
elementary reaction diffusion model [9.878043289026731]
Transition state (TS) search is key in chemistry for elucidating reaction mechanisms and exploring reaction networks.
Here, we develop an object-aware SE(3) equivariant diffusion model that satisfies all physical symmetries and constraints for generating sets of structures in an elementary reaction.
provided reactant and product, this model generates a TS structure in seconds instead of hours required when performing quantum chemistry-based optimizations.
arXiv Detail & Related papers (2023-04-12T22:21:36Z) - Multi-level Protocol for Mechanistic Reaction Studies Using Semi-local
Fitted Potential Energy Surfaces [0.0]
We propose a multi-scale protocol for routine theoretical studies of chemical reaction mechanisms.
The key aspect of the method's performance is its multi-scale nature, which not only saves computational effort but also allows extracting meaningful information.
arXiv Detail & Related papers (2023-04-03T12:55:29Z) - Self-Improved Retrosynthetic Planning [66.5397931294144]
Retrosynthetic planning is a fundamental problem in chemistry for finding a pathway of reactions to synthesize a target molecule.
Recent search algorithms have shown promising results for solving this problem by using deep neural networks (DNNs)
We propose an end-to-end framework for directly training the DNNs towards generating reaction pathways with the desirable properties.
arXiv Detail & Related papers (2021-06-09T08:03:57Z) - RetroXpert: Decompose Retrosynthesis Prediction like a Chemist [60.463900712314754]
We devise a novel template-free algorithm for automatic retrosynthetic expansion.
Our method disassembles retrosynthesis into two steps.
While outperforming the state-of-the-art baselines, our model also provides chemically reasonable interpretation.
arXiv Detail & Related papers (2020-11-04T04:35:34Z) - Towards the design of chemical reactions: Machine learning barriers of
competing mechanisms in reactant space [0.0]
We introduce a reactant-to-barrier (R2B) machine learning model that rapidly and accurately infers activation energies and transition state geometries.
R2B enjoys improving accuracy as training sets grow, and requires as input solely molecular graph information of the reactant.
We provide numerical evidence for the applicability of R2B for two competing text-book reactions relevant to organic synthesis, E2 and SN2.
arXiv Detail & Related papers (2020-09-28T15:50:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.