ArticleACS omega2023
Machine Learning C-N Couplings: Obstacles for a General-Purpose Reaction Yield Prediction.
Article in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Which Reaction Conditions Work on Drug-Like Molecules? Lessons from 66,000 High-Throughput Experiments.ACS central science · 2026Article
- Design and Experimental Validation of a Photocatalyst Recommender Based on a Large Language Model.Angewandte Chemie (International ed. in English) · 2026Article
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- Highly parallel optimisation of chemical reactions through automation and machine intelligence.Nature communications · 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
- Simple User-Friendly Reaction Format.Molecular informatics · 2025Article
- Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case.Angewandte Chemie (International ed. in English) · 2024Article
- Estimation of multicomponent reactions' yields from networks of mechanistic steps.Nature communications · 2024Article
- Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts.Chemical science · 2024Article
- Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry.RSC medicinal chemistry · 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
- ORDerly: Data Sets and Benchmarks for Chemical Reaction Data.Journal of chemical information and modeling · 2024Article
- A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Prediction of chemical reaction yields with large-scale multi-view pre-training.Journal of cheminformatics · 2024Article
- When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges.Journal of chemical information and modeling · 2024Review
- Machine learning-guided strategies for reaction conditions design and optimization.Beilstein journal of organic chemistry · 2024Review
- Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit.Journal of chemical information and modeling · 2023Review
- On the use of real-world datasets for reaction yield prediction.Chemical science · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pd-catalyzed C-N couplings are commonplace in academia and industry. Despite their significance, finding suitable reaction conditions leading to a high yield, for instance, remains a challenging and time-consuming task which usually requires screening over many sets of conditions. To help select promising reaction conditions in the vast space of reagent combinations, machine learning is an emerging technique with a lot of promise. In this work, we assess whether the reaction yield of C-N couplings can be predicted from databases of chemical reactions. We test the generalizability of models both on challenging data splits and on a dedicated experimental test set. We find that, provided the chemical space represented by the training set is not left, the models perform well. However, the applicability domain is quickly left even for simple reactions of the same type, as, for instance, present in our plate test set. The results show that yield prediction for new reactions is possible from the algorithmic side but in practice is hindered by the available data. Most importantly, more data that cover the diversity in reagents are needed for a general-purpose prediction of reaction yields. Our findings also expose a challenge to this field in that it appears to be extremely deceiving to judge models based on literature data with test sets which are split off the same literature data, even when challenging splits are considered.
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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.