ArticleNature communications2023
Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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Who cites it
20 citing papers in PubMed.
- Bridging Chemical Literature and Molecular Graphs: LLM-Augmented Enantioselectivity Prediction for Iridium-Catalyzed Asymmetric C-H Borylation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Resolving Chemically InequivalentJACS Au · 2026Article
- Transforming Molecular Synthesis With Large Language Models.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- Benchmarking molecular representations and machine learning algorithms for asymmetric catalysis: a palladium-catalysed decarboxylative asymmetric allylic alkylation case study.Journal of cheminformatics · 2026Article
- A general-purpose framework for chemical reaction representation with atomic correspondence and flexible condition adaptation.Journal of cheminformatics · 2026Article
- Staged Diversity-Constrained Machine Learning for High-Dimensional Reaction Condition Optimization.Angewandte Chemie (International ed. in English) · 2026Article
- Article
- log-RRIM: Yield Prediction via Local-to-Global Reaction Representation Learning and Interaction Modeling.Journal of chemical information and modeling · 2025Article
- Molecular Machine Learning Approach to Enantioselective C-H Bond Activation Reactions: From Generative AI to Experimental Validation.Chemical science · 2025Article
- Local reaction condition optimization via machine learning.Journal of molecular modeling · 2025Review
- Transfer learning across different photocatalytic organic reactions.Nature communications · 2025Article
- Computational tools for the prediction of site- and regioselectivity of organic reactions.Chemical science · 2025Review
- Article
- In-depth insight into structure-reactivity/regioselectivity relationship of Lewis acid catalyzed cascade 4πe-cyclization/dicycloexpansion reaction.Nature communications · 2025Article
- Recommending reaction conditions with label ranking.Chemical science · 2025Article
- QM9star, two Million DFT-computed Equilibrium Structures for Ions and Radicals with Atomic Information.Scientific data · 2024Article
- Deep Kernel learning for reaction outcome prediction and optimization.Communications chemistry · 2024Article
- Machine learning-guided strategies for reaction conditions design and optimization.Beilstein journal of organic chemistry · 2024Review
- Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis.Beilstein journal of organic chemistry · 2024Review
- Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge.Nature communications · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Accurate prediction of reactivity and selectivity provides the desired guideline for synthetic development. Due to the high-dimensional relationship between molecular structure and synthetic function, it is challenging to achieve the predictive modelling of synthetic transformation with the required extrapolative ability and chemical interpretability. To meet the gap between the rich domain knowledge of chemistry and the advanced molecular graph model, herein we report a knowledge-based graph model that embeds the digitalized steric and electronic information. In addition, a molecular interaction module is developed to enable the learning of the synergistic influence of reaction components. In this study, we demonstrate that this knowledge-based graph model achieves excellent predictions of reaction yield and stereoselectivity, whose extrapolative ability is corroborated by additional scaffold-based data splittings and experimental verifications with new catalysts. Because of the embedding of local environment, the model allows the atomic level of interpretation of the steric and electronic influence on the overall synthetic performance, which serves as a useful guide for the molecular engineering towards the target synthetic function. This model offers an extrapolative and interpretable approach for reaction performance prediction, pointing out the importance of chemical knowledge-constrained reaction modelling for synthetic purpose.
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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.