ReviewChemistry, an Asian journal2022
Machine Learning Applications for Chemical Reactions.
Review in Chemistry, an Asian journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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Who cites it
10 citing papers in PubMed.
- Transforming Molecular Science With Large Language Models: From Molecule Understanding to Autonomous Scientific Discovery.Chemistry, an Asian journal · 2026Review
- A Comprehensive Dataset of Chemical Reactions Covering Second and Third Row Elements with Million-Scale Quantum Chemical Calculations.Scientific data · 2026Article
- Interpretable machine learning for optimized dimethyl ether production from bio-methanol.Scientific reports · 2026Article
- A Review of Global Optimization Methods for Molecular Structures: Algorithms, Applications and Perspectives.Journal of computational chemistry · 2025Review
- Deep Reinforcement Learning-Based Self-Optimization of Flow Chemistry.ACS engineering Au · 2025Article
- Deep Learning Reaction Framework (DLRN) for kinetic modeling of time-resolved data.Communications chemistry · 2025Article
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
- LinChemIn: SynGraph-a data model and a toolkit to analyze and compare synthetic routes.Journal of cheminformatics · 2023Article
- Prediction of transition state structures of gas-phase chemical reactions via machine learning.Nature communications · 2023Article
- Machine Learning Applications for Chemical Reactions.Chemistry, an Asian journal · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Machine learning (ML) approaches have enabled rapid and efficient molecular property predictions as well as the design of new novel materials. In addition to great success for molecular problems, ML techniques are applied to various chemical reaction problems that require huge costs to solve with the existing experimental and simulation methods. In this review, starting with basic representations of chemical reactions, we summarized recent achievements of ML studies on two different problems; predicting reaction properties and synthetic routes. The various ML models are used to predict physical properties related to chemical reaction properties (e. g. thermodynamic changes, activation barriers, and reaction rates). Furthermore, the predictions of reactivity, self-optimization of reaction, and designing retrosynthetic reaction paths are also tackled by ML approaches. Herein we illustrate various ML strategies utilized in the various context of chemical reaction studies.
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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.