ArticleChemical science2021
Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts.
Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 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
- Evaluation of Machine Learning Models for Condition Optimization in Diverse Amide Coupling Reactions.ACS omega · 2026Article
- A machine learning-based workflow for transaminase selection.Chemical science · 2026Article
- Staged Diversity-Constrained Machine Learning for High-Dimensional Reaction Condition Optimization.Angewandte Chemie (International ed. in English) · 2026Article
- Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices.ACS catalysis · 2026Article
- AI Approaches to Homogeneous Catalysis with Transition Metal Complexes.ACS catalysis · 2025Review
- Data-Driven Virtual Screening of Conformational Ensembles of Transition-Metal Complexes.Journal of chemical theory and computation · 2025Article
- The Importance of Atomic Charges for Predicting Site-Selective Ir-, Ru-, and Rh-Catalyzed C-H Borylations.The Journal of organic chemistry · 2025Article
- 3DReact: Geometric Deep Learning for Chemical Reactions.Journal of chemical information and modeling · 2024Article
- Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity.ACS catalysis · 2024Article
- Impact of Model Selection and Conformational Effects on the Descriptors for In Silico Screening Campaigns: A Case Study of Rh-Catalyzed Acrylate Hydrogenation.The journal of physical chemistry. C, Nanomaterials and interfaces · 2024Article
- Benchmarking machine-readable vectors of chemical reactions on computed activation barriers.Digital discovery · 2024Article
- A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target.Chemical science · 2024Article
- On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set.ACS omega · 2024Article
- Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis.Beilstein journal of organic chemistry · 2024Review
- Feed-Forward Neural Network for Predicting Enantioselectivity of the Asymmetric Negishi Reaction.ACS central science · 2023Article
- Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge.Nature communications · 2023Article
- Navigating with chemometrics and machine learning in chemistry.Artificial intelligence review · 2023Article
- OSCAR: an extensive repository of chemically and functionally diverse organocatalysts.Chemical science · 2022Article
- Theoretical Perspectives in Organocatalysis.Chemistry (Weinheim an der Bergstrasse, Germany) · 2022Review
Corrections and comments
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically discovered through experimental screening. Recent advances in combining quantum chemical computations and machine learning (ML) hold great potential to propel the next leap forward in asymmetric catalysis. Within the context of quantum chemical machine learning (QML, or atomistic ML), the ML representations used to encode the three-dimensional structure of molecules and evaluate their similarity cannot easily capture the subtle energy differences that govern enantioselectivity. Here, we present a general strategy for improving molecular representations within an atomistic machine learning model to predict the DFT-computed enantiomeric excess of asymmetric propargylation organocatalysts solely from the structure of catalytic cycle intermediates. Mean absolute errors as low as 0.25 kcal mol
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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.