Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki-Miyaura Coupling

Wiktor Beker, Rafal Roszak, Agnieszka Wolos, Nicholas H. Angello, Vandana Rathore, Martin D. Burke, Bartosz A. Grzybowski

Research output: Contribution to journalArticlepeer-review

Abstract

Applications of machine learning (ML) to synthetic chemistry rely on the assumption that large numbers of literature-reported examples should enable construction of accurate and predictive models of chemical reactivity. This paper demonstrates that abundance of carefully curated literature data may be insufficient for this purpose. Using an example of Suzuki-Miyaura coupling with heterocyclic building blocks─and a carefully selected database of >10,000 literature examples─we show that ML models cannot offer any meaningful predictions of optimum reaction conditions, even if the search space is restricted to only solvents and bases. This result holds irrespective of the ML model applied (from simple feed-forward to state-of-the-art graph-convolution neural networks) or the representation to describe the reaction partners (various fingerprints, chemical descriptors, latent representations, etc.). In all cases, the ML methods fail to perform significantly better than naive assignments based on the sheer frequency of certain reaction conditions reported in the literature. These unsatisfactory results likely reflect subjective preferences of various chemists to use certain protocols, other biasing factors as mundane as availability of certain solvents/reagents, and/or a lack of negative data. These findings highlight the likely importance of systematically generating reliable and standardized data sets for algorithm training.

Original languageEnglish (US)
Pages (from-to)4819-4827
Number of pages9
JournalJournal of the American Chemical Society
Volume144
Issue number11
DOIs
StatePublished - Mar 23 2022

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry
  • Catalysis
  • Colloid and Surface Chemistry

Fingerprint

Dive into the research topics of 'Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki-Miyaura Coupling'. Together they form a unique fingerprint.

Cite this