ArticleProceedings of the National Academy of Sciences of the United States of America2023
Digitizing chemical discovery with a Bayesian explorer for interpreting reactivity data.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
- Staged Diversity-Constrained Machine Learning for High-Dimensional Reaction Condition Optimization.Angewandte Chemie (International ed. in English) · 2026Article
- Utilizing Similarity Measures to Map Chemical Reactivity.Angewandte Chemie (International ed. in English) · 2026Article
- AI and automation: democratizing automation and the evolution towards true AI-autonomous robotics.Chemical science · 2025Review
- Article
- IvoryOS: an interoperable web interface for orchestrating Python-based self-driving laboratories.Nature communications · 2025Article
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- Digitizing chemical discovery with a Bayesian explorer for interpreting reactivity data.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Interpreting the outcome of chemistry experiments consistently is slow and frequently introduces unwanted hidden bias. This difficulty limits the scale of collectable data and often leads to exclusion of negative results, which severely limits progress in the field. What is needed is a way to standardize the discovery process and accelerate the interpretation of high-dimensional data aided by the expert chemist's intuition. We demonstrate a digital Oracle that interprets chemical reactivity using probability. By carrying out >500 reactions covering a large space and retaining both the positive and negative results, the Oracle was able to rediscover eight historically important reactions including the aldol condensation, Buchwald-Hartwig amination, Heck, Mannich, Sonogashira, Suzuki, Wittig, and Wittig-Horner reactions. This paradigm for decoding reactivity validates and formalizes the expert chemist's experience and intuition, providing a quantitative criterion of discovery scalable to all available experimental data.
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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.