ArticleRSC medicinal chemistry2024
Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry.
Article in RSC medicinal chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Evaluation of Machine Learning Models for Condition Optimization in Diverse Amide Coupling Reactions.ACS omega · 2026Article
- Frugal Sampling Strategies for Navigating Complex Reaction Spaces.Organic process research & development · 2026Article
- Which Reaction Conditions Work on Drug-Like Molecules? Lessons from 66,000 High-Throughput Experiments.ACS central science · 2026Article
- Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization.Nature communications · 2025Article
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- Highly parallel optimisation of chemical reactions through automation and machine intelligence.Nature communications · 2025Article
- CombiningRSC advances · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Suzuki cross-coupling reactions are considered a valuable tool for constructing carbon-carbon bonds in small molecule drug discovery. However, the synthesis of chemical matter often represents a time-consuming and labour-intensive bottleneck. We demonstrate how machine learning methods trained on high-throughput experimentation (HTE) data can be leveraged to enable fast reaction condition selection for novel coupling partners. We show that the trained models support chemists in determining suitable catalyst-solvent-base combinations for individual transformations including an evaluation of the need for HTE screening. We introduce an algorithm for designing 96-well plates optimized towards reaction yields and discuss the model performance of zero- and few-shot machine learning. The best-performing machine learning model achieved a three-category classification accuracy of 76.3% (±0.2%) and an
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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.