ArticleThe journal of physical chemistry. A2024
Quantitative Structure-Reactivity Relationships for Synthesis Planning: The Benzhydrylium Case.
Article in The journal of physical chemistry. A, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Uncertainty Quantification forChemical reviews · 2026Review
- Predicting the Stability of Base-mediated C─H Carboxylation Adducts Using Data Science Tools.Angewandte Chemie (International ed. in English) · 2026Article
- Predicting and Explaining Yields with Machine Learning for Carboxylated Azoles and Beyond.Journal of chemical information and modeling · 2025Article
- Quantitative Structure-Reactivity Relationships for Synthesis Planning: The Benzhydrylium Case.The journal of physical chemistry. A · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Selective and feasible reactions are among the top targets in synthesis planning. Mayr's approach to quantifying chemical reactivity has greatly facilitated the planning process, but reactivity parameters for new compounds require time-consuming experiments. In the past decade, data-driven modeling has been gaining momentum in the field, as it shows promise in terms of efficient reactivity prediction. However, state-of-the-art models use quantum chemical data as input, which prevent access to real-time planning in organic synthesis. Here, we present a novel data-driven workflow for predicting reactivity parameters of molecules that takes only structural information as input, enabling
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Registered trials
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