ArticleChemical science2023
On the use of real-world datasets for reaction yield prediction.
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 46 papers.
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Who cites it
46 citing papers in PubMed.
- Time series data with and without anomalies from a continuous distillation plant.Scientific data · 2026Article
- Robust out-of-distribution prediction of Buchwald-Hartwig reactions.Nature computational science · 2026Article
- Hyp-retro: hypernetwork-guided preference-conditioned retrosynthetic planner.Molecular diversity · 2026Article
- Yield Smarter, Not Harder: Good Practices for Machine Learning of Reaction Outcomes.Journal of the American Chemical Society · 2026Article
- Yield Prediction of Organic Reactions in Biased Data Sets via Positive-Unlabeled Learning.Journal of the American Chemical Society · 2026Article
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- 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
- Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices.ACS catalysis · 2026Article
- Article
- Digital Twin for Chemical Science: a case study on water interactions on the Ag(111) surface.Nature computational science · 2025Article
- log-RRIM: Yield Prediction via Local-to-Global Reaction Representation Learning and Interaction Modeling.Journal of chemical information and modeling · 2025Article
- Study on Phosphorus Compound/Catechol-Catalyzed Dehydrative Amidation and Its Database Development for Machine Learning.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Article
- Molecular Machine Learning Approach to Enantioselective C-H Bond Activation Reactions: From Generative AI to Experimental Validation.Chemical science · 2025Article
- Developing Pharmaceutically Relevant Pd-Catalyzed C-N Coupling Reactivity Models Leveraging High-Throughput Experimentation.Journal of the American Chemical Society · 2025Article
- AI Approaches to Homogeneous Catalysis with Transition Metal Complexes.ACS catalysis · 2025Review
- 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
- Predicting three-component reaction outcomes from ~40,000 miniaturized reactant combinations.Science advances · 2025Article
- Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning.Nature communications · 2025Article
- Carbohydrate Synthesis is Entering the Data-Driven Digital Era.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application of machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such datasets have been made publicly available. The first real-world dataset from the ELNs of a large pharmaceutical company is disclosed and its relationship to high-throughput experimentation (HTE) datasets is described. For chemical yield predictions, a key task in chemical synthesis, an attributed graph neural network (AGNN) performs as well as or better than the best previous models on two HTE datasets for the Suzuki-Miyaura and Buchwald-Hartwig reactions. However, training the AGNN on an ELN dataset does not lead to a predictive model. The implications of using ELN data for training ML-based models are discussed in the context of yield predictions.
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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.