ArticleChemical science2020
Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors.
Article in Chemical science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- BONAFIDE: a Python framework for the calculation of local features for atoms and bonds in molecules.Journal of cheminformatics · 2026Article
- AGAPE (Computational G‑Quadruplex Stabilization Prediction): The First Machine Learning Workflow for G‑Quadruplex Stabilization Prediction.ACS omega · 2026Article
- Rega: A Platform for the Prediction of the Regioselectivity of C-H Functionalization Reactions.Journal of chemical information and modeling · 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
- Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction.Digital discovery · 2026Article
- Effect of Molecular Descriptors on the Development of Machine Learning Models for the Prediction of Yield Sooting Index.ACS omega · 2025Article
- Graph neural networks for predicting metal-ligand coordination of transition metal complexes.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Quantum Descriptor-Based Machine-Learning Modeling of Thermal Hazard of Cyclic Sulfamidates.Journal of chemical information and modeling · 2025Article
- Developing Pharmaceutically Relevant Pd-Catalyzed C-N Coupling Reactivity Models Leveraging High-Throughput Experimentation.Journal of the American Chemical Society · 2025Article
- ASKCOS: Open-Source, Data-Driven Synthesis Planning.Accounts of chemical research · 2025Article
- Benchmarking molecular conformer augmentation with context-enriched training: graph-based transformer versus GNN models.Journal of cheminformatics · 2025Article
- The Importance of Atomic Charges for Predicting Site-Selective Ir-, Ru-, and Rh-Catalyzed C-H Borylations.The Journal of organic chemistry · 2025Article
- Computational tools for the prediction of site- and regioselectivity of organic reactions.Chemical science · 2025Review
- Article
- Pretraining graph transformers with atom-in-a-molecule quantum properties for improved ADMET modeling.Journal of cheminformatics · 2025Article
- Simple User-Friendly Reaction Format.Molecular informatics · 2025Article
- AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning.Journal of chemical information and modeling · 2024Article
- Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry.RSC medicinal chemistry · 2024Article
- Bottom-Up Atomistic Descriptions of Top-Down Macroscopic Measurements: Computational Benchmarks for Hammett Electronic Parameters.ACS physical chemistry Au · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate and rapid evaluation of whether substrates can undergo the desired the transformation is crucial and challenging for both human knowledge and computer predictions. Despite the potential of machine learning in predicting chemical reactivity such as selectivity, popular feature engineering and learning methods are either time-consuming or data-hungry. We introduce a new method that combines machine-learned reaction representation with selected quantum mechanical descriptors to predict regio-selectivity in general substitution reactions. We construct a reactivity descriptor database based on
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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.