Journal ArticleDOI
Scaffold‐directed face selectivity Machine‐Learned from vectors of non‐covalent interactions
TLDR
In this article, a method to vectorize and machine-learn non-covalent interactions responsible for scaffold-directed reactions important in synthetic chemistry is described, and models trained on this representation predict correct face of approach in ca. 90% of Michael additions or Diels-Alder cycloadditions.Abstract:
This work describes a method to vectorize and Machine-Learn, ML, non-covalent interactions responsible for scaffold-directed reactions important in synthetic chemistry. Models trained on this representation predict correct face of approach in ca. 90 % of Michael additions or Diels-Alder cycloadditions. These accuracies are significantly higher than those based on traditional ML descriptors, energetic calculations, or intuition of experienced synthetic chemists. Our results also emphasize the importance of ML models being provided with relevant mechanistic knowledge; without such knowledge, these models cannot easily "transfer-learn" and extrapolate to previously unseen reaction mechanisms.read more
Citations
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Journal ArticleDOI
Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura Coupling
Wiktor Beker,Rafał Roszak,Agnieszka Wolos,Nicholas H. Angello,Vandana Rathore,Martin D. Burke,Bartosz A. Grzybowski +6 more
TL;DR: It is shown that ML models cannot offer any meaningful predictions of optimum reaction conditions, even if the search space is restricted to only solvents and bases, and highlighted the likely importance of systematically generating reliable and standardized data sets for algorithm training.
Journal ArticleDOI
Towards Data-Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning
TL;DR: In this article, the authors developed a hierarchical learning approach to achieve predictive machine leaning model using only dozens of enantioselectivity data with the target olefin, which offers a useful solution for the few-shot learning problem.
Journal ArticleDOI
Is Organic Chemistry Really Growing Exponentially
TL;DR: Trends in the function of time, reaction-type "popularity" and complexity based on the algorithm that extracts generalized reaction class templates are studied, useful in the context of computer-assisted synthesis, machine learning, and also for identifying erroneous entries in reaction databases.
Journal ArticleDOI
Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands.
Jordan J. Dotson,Lucy van Dijk,Jacob C. Timmerman,Samantha Grosslight,Richard C. Walroth,Francis Gosselin,Kurt Püntener,Kyle A. Mack,Matthew S. Sigman +8 more
TL;DR: In this article , a machine learning workflow for the multi-objective optimization of catalytic reactions that employ chiral bisphosphine ligands was described through the optimization of two sequential reactions required in the asymmetric synthesis of an active pharmaceutical ingredient.
Journal ArticleDOI
Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis
TL;DR: A recent review as mentioned in this paper summarizes the cutting-edge embedding techniques and model designs in synthetic performance prediction, elaborating how chemical knowledge can be incorporated into machine learning until June 2022.
References
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