ReviewNature reviews. Chemistry2021
Organic reactivity from mechanism to machine learning.
Review in Nature reviews. Chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 39 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
39 citing papers in PubMed.
- Chemistry-informed Bayesian control and policy distillation for autonomous pH adjustment in diverse chemical systems.Chemical science · 2026Article
- BONAFIDE: a Python framework for the calculation of local features for atoms and bonds in molecules.Journal of cheminformatics · 2026Article
- Enhancing molecular property prediction of transformer models with dual graph representation.Nature communications · 2026Article
- Extracting Mechanistic Information from an Open Data Set for a Pharma-Relevant Suzuki-Miyaura Cross-Coupling Reaction.Organic process research & development · 2026Article
- Adaptive Transition-State Refinement with Learned Equilibrium Flows.Journal of chemical information and modeling · 2026Article
- Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices.ACS catalysis · 2026Article
- Harnessing Machine Learning to Enhance Transition State Search with Interatomic Potentials and Generative Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Study on Phosphorus Compound/Catechol-Catalyzed Dehydrative Amidation and Its Database Development for Machine Learning.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Article
- Discovery of NiJournal of the American Chemical Society · 2025Article
- Advances in the Design and Discovery of Organic Semiconductors Aided by Machine Learning.Annual review of materials research · 2025Article
- Organometallic-type reactivity of stable organoboronates for selective (hetero)arene C-H/C-halogen borylation and beyond.Nature communications · 2025Article
- Development and Validation of Atomic Group Descriptors for Substituent Effects.Journal of computational chemistry · 2025Article
- SPOCK Tool for Constructing Empirical Volcano Diagrams from Catalytic Data.ACS catalysis · 2025Article
- Computational tools for the prediction of site- and regioselectivity of organic reactions.Chemical science · 2025Review
- A Holistic Data-Driven Approach to Synthesis Predictions of Colloidal Nanocrystal Shapes.Journal of the American Chemical Society · 2025Article
- Data Generation for Machine Learning Interatomic Potentials and Beyond.Chemical reviews · 2024Review
- Article
- Continuous flow synthesis enabling reaction discovery.Chemical science · 2024Review
- A physical organic strategy to predict and interpret stabilities of chemical bonds in energetic compounds for the discovery of thermal-resistant properties.Journal of molecular modeling · 2024Article
- Accelerated chemical science with AI.Digital discovery · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As more data are introduced in the building of models of chemical reactivity, the mechanistic component can be reduced until 'big data' applications are reached. These methods no longer depend on underlying mechanistic hypotheses, potentially learning them implicitly through extensive data training. Reactivity models often focus on reaction barriers, but can also be trained to directly predict lab-relevant properties, such as yields or conditions. Calculations with a quantum-mechanical component are still preferred for quantitative predictions of reactivity. Although big data applications tend to be more qualitative, they have the advantage to be broadly applied to different kinds of reactions. There is a continuum of methods in between these extremes, such as methods that use quantum-derived data or descriptors in machine learning models. Here, we present an overview of the recent machine learning applications in the field of chemical reactivity from a mechanistic perspective. Starting with a summary of how reactivity questions are addressed by quantum-mechanical methods, we discuss methods that augment or replace quantum-based modelling with faster alternatives relying on machine learning.
Identifiers
37117288What 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.