Quantitative Structure-Reactivity Relationships for Synthesis Planning: The Benzhydrylium Case

Area of Science:

• Organic Chemistry
• Computational Chemistry
• Chemical Informatics

Background:

• Chemical reactivity prediction is crucial for synthesis planning.
• Current methods like Mayr's approach require extensive experiments, while data-driven models often rely on computationally expensive quantum chemical data.
• Existing models hinder real-time application in organic synthesis planning.

Purpose of the Study:

• To develop a novel data-driven workflow for predicting molecular reactivity parameters.
• To enable real-time reactivity predictions using only structural information as input.
• To demonstrate the approach's functionality and the performance of quantitative structure-reactivity relationships (QSRRs) using benzhydrylium ions.

Main Methods:

• Developed a data-driven workflow that utilizes molecular structural information exclusively.
• Applied the workflow to the chemical space of benzhydrylium ions.
• Generated and evaluated quantitative structure-reactivity relationships (QSRRs).

Main Results:

• Achieved de facto real-time reactivity predictions based solely on molecular structure.
• Demonstrated the straightforward development of low-cost QSRR models that are accurate, interpretable, and transferable.
• Showcased that Hammett σ parameters exhibit only approximate additivity within the studied system.

Conclusions:

• The novel workflow enables efficient and real-time prediction of chemical reactivity, significantly advancing synthesis planning.
• Low-cost, accurate, interpretable, and transferable QSRR models can be readily built using structural data.
• The findings challenge the assumption of strict additivity for certain reactivity parameters, offering new insights into chemical behavior.