ArticleNature2014
Interrogating selectivity in catalysis using molecular vibrations.
Article in Nature, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 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
58 citing papers in PubMed.
- Chemodivergent aminocarbonylation enabled by oxygen vacancy-engineered Pd-doped InScience advances · 2026Article
- One pot sulfur(IV) fluoride-mediated deoxyaminations for the syntheses of 3,3-arylamino oxetanes and azetidines.Communications chemistry · 2026Article
- Article
- Stereoselective Generalizations over Diverse Sets of Chiral Acids Enabled by Buried Volume.Journal of the American Chemical Society · 2026Article
- AI Approaches to Homogeneous Catalysis with Transition Metal Complexes.ACS catalysis · 2025Review
- Computational tools for the prediction of site- and regioselectivity of organic reactions.Chemical science · 2025Review
- Systematic Parameter Determination Aimed at a Catalyst-Controlled Asymmetric Rh(I)-Catalyzed Pauson-Khand Reaction.ACS catalysis · 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
- Predicting success in Cu-catalyzed C-N coupling reactions using data science.Science advances · 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
- Molecular Dynamics of Artificially Pair-Decoupled Systems: An Accurate Tool for Investigating the Importance of Intramolecular Couplings.Journal of chemical theory and computation · 2023Article
- A substrate descriptor based approach for the prediction and understanding of the regioselectivity in caged catalyzed hydroformylation.Faraday discussions · 2023Article
- Amino Acid-Derived Ionic Chiral Catalysts Enable Desymmetrizing Cross-Coupling to Remote Acyclic Quaternary Stereocenters.Journal of the American Chemical Society · 2023Article
- Article
- Analysis of vibronic coupling in a 4f molecular magnet with FIRMS.Nature communications · 2022Article
- Effect of Solvents on Proline Modified at the Secondary Sphere: A Multivariate Exploration.The Journal of organic chemistry · 2022Article
- Tuning the Properties of Protein-Based Polymers Using High-Performance Orthogonal Translation Systems for the Incorporation of Aromatic Non-Canonical Amino Acids.Frontiers in bioengineering and biotechnology · 2022Article
- Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts.Chemical science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The delineation of molecular properties that underlie reactivity and selectivity is at the core of physical organic chemistry, and this knowledge can be used to inform the design of improved synthetic methods or identify new chemical transformations. For this reason, the mathematical representation of properties affecting reactivity and selectivity trends, that is, molecular parameters, is paramount. Correlations produced by equating these molecular parameters with experimental outcomes are often defined as free-energy relationships and can be used to evaluate the origin of selectivity and to generate new, experimentally testable hypotheses. The premise behind successful correlations of this type is that a systematically perturbed molecular property affects a transition-state interaction between the catalyst, substrate and any reaction components involved in the determination of selectivity. Classic physical organic molecular descriptors, such as Hammett, Taft or Charton parameters, seek to independently probe isolated electronic or steric effects. However, these parameters cannot address simultaneous, non-additive variations to more than one molecular property, which limits their utility. Here we report a parameter system based on the vibrational response of a molecule to infrared radiation that can be used to mathematically model and predict selectivity trends for reactions with interlinked steric and electronic effects at positions of interest. The disclosed parameter system is mechanistically derived and should find broad use in the study of chemical and biological systems.
Indexed as
Identifiers
24622199What 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.