ArticleChemical science2019
Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis.
Article in Chemical science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Developing Sustainable Chemistry with AI.Accounts of chemical research · 2026Article
- Benchmarking molecular representations and machine learning algorithms for asymmetric catalysis: a palladium-catalysed decarboxylative asymmetric allylic alkylation case study.Journal of cheminformatics · 2026Article
- AGAPE (Computational G‑Quadruplex Stabilization Prediction): The First Machine Learning Workflow for G‑Quadruplex Stabilization Prediction.ACS omega · 2026Article
- A Solvent Selection Framework for Porous Organic Polymers.Journal of chemical information and modeling · 2025Article
- Predictive design of crystallographic chiral separation.Nature communications · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Machine Learning-Driven Optimization of Continuous-Flow Photoredox Amine Synthesis.Organic process research & development · 2025Article
- Deep Reinforcement Learning-Based Self-Optimization of Flow Chemistry.ACS engineering Au · 2025Article
- Surfactant representation using COSMO screened charge density for adsorption isotherm prediction using Physics-Informed Neural Network (PINN).Journal of cheminformatics · 2025Article
- Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies.Chem & bio engineering · 2025Review
- Substrate specific closed-loop optimization of carbohydrate protective group chemistry using Bayesian optimization and transfer learning.Chemical science · 2023Article
- Accelerated Chemical Reaction Optimization Using Multi-Task Learning.ACS central science · 2023Article
- A Brief Introduction to Chemical Reaction Optimization.Chemical reviews · 2023Review
- Transferrable selectivity profiles enable prediction in synergistic catalyst space.Chemical science · 2023Article
- Prediction and Construction of Energetic Materials Based on Machine Learning Methods.Molecules (Basel, Switzerland) · 2022Review
- Autonomous Multi-Step and Multi-Objective Optimization Facilitated by Real-Time Process Analytics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Directional emission of white light via selective amplification of photon recycling and Bayesian optimization of multi-layer thin films.Scientific reports · 2022Article
- Replacement of Less-Preferred Dipolar Aprotic and Ethereal Solvents in Synthetic Organic Chemistry with More Sustainable Alternatives.Chemical reviews · 2022Review
- Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts.Chemical science · 2021Article
- Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex.Chemical science · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rational solvent selection remains a significant challenge in process development. Here we describe a hybrid mechanistic-machine learning approach, geared towards automated process development workflow. A library of 459 solvents was used, for which 12 conventional molecular descriptors, two reaction-specific descriptors, and additional descriptors based on screening charge density, were calculated. Gaussian process surrogate models were trained on experimental data from a Rh(CO)
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