ArticleScience advances2024
Predicting success in Cu-catalyzed C-N coupling reactions using data science.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The trial behind it
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Who cites it
18 citing papers in PubMed.
- Origin of Scission Selectivity for Phosphoranyl Radicals in Photoredox Catalysis.Journal of the American Chemical Society · 2026Article
- Base-Free C-N Cross-Electrophile Coupling.Journal of the American Chemical Society · 2026Article
- Improvements in chemical reaction pathway exploration algorithms and dataset generation.Scientific data · 2026Article
- A machine learning-based workflow for transaminase selection.Chemical science · 2026Article
- A 50,688-Reaction Data Set Reveals General Ligands and Mechanistic Diversity in C-N Couplings.Journal of the American Chemical Society · 2026Article
- Frugal Sampling Strategies for Navigating Complex Reaction Spaces.Organic process research & development · 2026Article
- Best Practices and Considerations for Applying Multiple Linear Regression in Organic Chemistry Research.The Journal of organic chemistry · 2026Review
- Which Reaction Conditions Work on Drug-Like Molecules? Lessons from 66,000 High-Throughput Experiments.ACS central science · 2026Article
- Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices.ACS catalysis · 2026Article
- Considerations in Pursuing Reaction Scope Generality.Angewandte Chemie (International ed. in English) · 2025Review
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- Bayesian Meta-Learning for Few-Shot Reaction Outcome Prediction of Asymmetric Hydrogenation of Olefins.Angewandte Chemie (International ed. in English) · 2025Article
- Applying Active Learning toward Building a Generalizable Model for Ni-Photoredox Cross-Electrophile Coupling of Aryl and Alkyl Bromides.Journal of the American Chemical Society · 2025Article
- Transfer learning across different photocatalytic organic reactions.Nature communications · 2025Article
- Recommending reaction conditions with label ranking.Chemical science · 2025Article
- Cross-Coupling Reactions with Nickel, Visible Light, andACS catalysis · 2025Article
- Rapid prediction of conformationally-dependent DFT-level descriptors using graph neural networks for carboxylic acids and alkyl amines.Digital discovery · 2025Article
- Applying statistical modeling strategies to sparse datasets in synthetic chemistry.Science advances · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Data science is assuming a pivotal role in guiding reaction optimization and streamlining experimental workloads in the evolving landscape of synthetic chemistry. A discipline-wide goal is the development of workflows that integrate computational chemistry and data science tools with high-throughput experimentation as it provides experimentalists the ability to maximize success in expensive synthetic campaigns. Here, we report an end-to-end data-driven process to effectively predict how structural features of coupling partners and ligands affect Cu-catalyzed C-N coupling reactions. The established workflow underscores the limitations posed by substrates and ligands while also providing a systematic ligand prediction tool that uses probability to assess when a ligand will be successful. This platform is strategically designed to confront the intrinsic unpredictability frequently encountered in synthetic reaction deployment.
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What 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.