ArticleACS central science2026
Which Reaction Conditions Work on Drug-Like Molecules? Lessons from 66,000 High-Throughput Experiments.
Article in ACS central science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
6 authors.
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Abstract
High-throughput experimentation (HTE) accelerates chemical discovery by shortening the lead times for molecule synthesis. The choice of initial reaction conditions directly influences the outcome and length of any reaction optimization. But human involvement in plate design and data analysis remains a significant cost factor and is accompanied by biases. Therefore, making the most out of past reaction outcomes is crucial. While advances in machine learning allow us to generate promising reaction conditions, this approach is often not suitable because not enough relevant reaction data are available or it is of insufficient quality. Herein we introduce a robust statistical method using
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Registered trials
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