ArticleChemical science2023
The challenge of balancing model sensitivity and robustness in predicting yields: a benchmarking study of amide coupling reactions.
Article in Chemical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 27 citations in OpenAlex.
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- Evaluation of Machine Learning Models for Condition Optimization in Diverse Amide Coupling Reactions.ACS omega · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Design and Experimental Validation of a Photocatalyst Recommender Based on a Large Language Model.Angewandte Chemie (International ed. in English) · 2026Article
- Automating structure-activity analysis for electrochemical nitrogen reduction catalyst design through multi-agent collaborations.National science review · 2025Article
- DeepEGFR a graph neural network for bioactivity classification of EGFR inhibitors.Scientific reports · 2025Article
- Fast and Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions.JACS Au · 2025Article
- Article
- Transfer learning across different photocatalytic organic reactions.Nature communications · 2025Article
- Systematic, computational discovery of multicomponent and one-pot reactions.Nature communications · 2024Article
- Estimation of multicomponent reactions' yields from networks of mechanistic steps.Nature communications · 2024Article
- Deep Kernel learning for reaction outcome prediction and optimization.Communications chemistry · 2024Article
- Incorporating Synthetic Accessibility in Drug Design: Predicting Reaction Yields of Suzuki Cross-Couplings by Leveraging AbbVie's 15-Year Parallel Library Data Set.Journal of the American Chemical Society · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate prediction of reaction yield is the holy grail for computer-assisted synthesis prediction, but current models have failed to generalize to large literature datasets. To understand the causes and inspire future design, we systematically benchmarked the yield prediction task. We carefully curated and augmented a literature dataset of 41 239 amide coupling reactions, each with information on reactants, products, intermediates, yields, and reaction contexts, and provided 3D structures for the molecules. We calculated molecular features related to 2D and 3D structure information, as well as physical and electronic properties. These descriptors were paired with 4 categories of machine learning methods (linear, kernel, ensemble, and neural network), yielding valuable benchmarks about feature and model performance. Despite the excellent performance on a high-throughput experiment (HTE) dataset (
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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.