ReviewChemical reviews2020
Quantitative Structure-Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future.
Review in Chemical reviews, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
- Competition-Derived Relative Reactivity and 3D Electronic-State Analysis of Site- and Facial Selectivity in NaBHJACS Au · 2026Article
- Automated transition state generation for mechanistic exploration in organic synthesis.Nature communications · 2026Article
- Transforming Molecular Synthesis With Large Language Models.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- Staged Diversity-Constrained Machine Learning for High-Dimensional Reaction Condition Optimization.Angewandte Chemie (International ed. in English) · 2026Article
- Stereodivergent Construction ofJournal of the American Chemical Society · 2026Article
- Size and Polarizability as Design Principles for Stereoselective Catalysis.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Review
- Article
- Understanding DFT Uncertainties for More Reliable Reactivity Predictions by Advancing the Analysis of Error Sources.Journal of chemical theory and computation · 2025Article
- Data-Driven Prediction of Enantioselectivity for the Sharpless Asymmetric Dihydroxylation: Model Development and Experimental Validation.ACS central science · 2025Article
- Chiral medium-sized rings beyond central chirality.Nature reviews. Chemistry · 2025Review
- Discovery of NiJournal of the American Chemical Society · 2025Article
- Mathematical Framework to Identify Optimal Molecule Based on Virtual Ligand Strategy.Journal of chemical information and modeling · 2025Article
- AI Approaches to Homogeneous Catalysis with Transition Metal Complexes.ACS catalysis · 2025Review
- Transfer Learning-Enabled Ligand Prediction for Ni-Catalyzed Atroposelective Suzuki-Miyaura Cross-Coupling Based on Mechanistic Similarity: Leveraging Pd Knowledge for Ni Discovery.Journal of the American Chemical Society · 2025Article
- Computational tools for the prediction of site- and regioselectivity of organic reactions.Chemical science · 2025Review
- Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts.Chemical science · 2024Article
- Stereoretentive enantioconvergent reactions.Nature chemistry · 2024Article
- Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity.ACS catalysis · 2024Article
- In-silico-assisted derivatization of triarylboranes for the catalytic reductive functionalization of aniline-derived amino acids and peptides with HNature communications · 2024Article
- Insights into the Regioselectivity of Metal-Catalyzed Aryne Reactions.Synlett : accounts and rapid communications in synthetic organic chemistry · 2024Article
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3 authors.
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Abstract
The dawn of the 21st century has brought with it a surge of research related to computer-guided approaches to catalyst design. In the past two decades, chemoinformatics, the application of informatics to solve problems in chemistry, has increasingly influenced prediction of activity and mechanistic investigations of organic reactions. The advent of advanced statistical and machine learning methods, as well as dramatic increases in computational speed and memory, has contributed to this emerging field of study. This review summarizes strategies to employ quantitative structure-selectivity relationships (QSSR) in asymmetric catalytic reactions. The coverage is structured by initially introducing the basic features of these methods. Subsequent topics are discussed according to increasing complexity of molecular representations. As the most applied subfield of QSSR in enantioselective catalysis, the application of local parametrization approaches and linear free energy relationships (LFERs) along with multivariate modeling techniques is described first. This section is followed by a description of global parametrization methods, the first of which is continuous chirality measures (CCM) because it is a single parameter derived from the global structure of a molecule. Chirality codes, global, multivariate descriptors, are then introduced followed by molecular interaction fields (MIFs), a global descriptor class that typically has the highest dimensionality. To highlight the current reach of QSSR in enantioselective transformations, a comprehensive collection of examples is presented. When combined with traditional experimental approaches, chemoinformatics holds great promise to predict new catalyst structures, rationalize mechanistic behavior, and profoundly change the way chemists discover and optimize reactions.
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