ArticleAccounts of chemical research2021
Data Science Meets Physical Organic Chemistry.
Article in Accounts of chemical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed.
- Competition-Derived Relative Reactivity and 3D Electronic-State Analysis of Site- and Facial Selectivity in NaBHJACS Au · 2026Article
- Catalytic Enantioselective Aziridination of Z-Disubstituted Alkenes with Aspartyl β-Turn-Based Dirhodium(II) Metallopeptides and Assessment of Catalyst Scaffold Versatility.Journal of the American Chemical Society · 2026Article
- Origin of Scission Selectivity for Phosphoranyl Radicals in Photoredox Catalysis.Journal of the American Chemical Society · 2026Article
- Chiral Analysis for High-Throughput Reaction Screening: Recent Advances in Accelerating Asymmetric Catalysis Discovery.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
- Mechanochemical ligand-controlled regiodivergent hydroarylation of alkenes via iron-catalyzed C-H activation.Nature communications · 2025Article
- Understanding and Enhancing Stereoselective Polymerization Using a Data Science Approach.Journal of the American Chemical Society · 2025Article
- Considerations in Pursuing Reaction Scope Generality.Angewandte Chemie (International ed. in English) · 2025Review
- Nickel-Mediated Radical Capture: Evidence for a Concerted Inner-Sphere Mechanism.Journal of the American Chemical Society · 2025Article
- Parametrization of κACS catalysis · 2025Article
- Applying statistical modeling strategies to sparse datasets in synthetic chemistry.Science advances · 2025Review
- Article
- Supramolecular Catalyzed Cascade Reduction of Azaarenes Interrogated via Data Science.Journal of the American Chemical Society · 2024Article
- Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts.Chemical science · 2024Article
- Automation and machine learning augmented by large language models in a catalysis study.Chemical science · 2024Review
- 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
- Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis.Beilstein journal of organic chemistry · 2024Review
- Dataset Design for Building Models of Chemical Reactivity.ACS central science · 2023Review
- Using Data Science for Mechanistic Insights and Selectivity Predictions in a Non-Natural Biocatalytic Reaction.Journal of the American Chemical Society · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
ConspectusAt the heart of synthetic chemistry is the holy grail of predictable catalyst design. In particular, researchers involved in reaction development in asymmetric catalysis have pursued a variety of strategies toward this goal. This is driven by both the pragmatic need to achieve high selectivities and the inability to readily identify why a certain catalyst is effective for a given reaction. While empiricism and intuition have dominated the field of asymmetric catalysis since its inception, enantioselectivity offers a mechanistically rich platform to interrogate catalyst-structure response patterns that explain the performance of a particular catalyst or substrate.In the early stages of an asymmetric reaction development campaign, the overarching mechanism of the reaction, catalyst speciation, the turnover limiting step, and many other details are unknown or posited based on related reactions. Considering the unclear details leading to a successful reaction, initial enantioselectivity data are often used to intuitively guide the ultimate direction of optimization. However, if the conditions of the Curtin-Hammett principle are satisfied, then measured enantioselectivity can be directly connected to the ensemble of diastereomeric transition states (TSs) that lead to the enantiomeric products, and the associated free energy difference between competing TSs (ΔΔ
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What OpenQuestion holds
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.