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Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands.

Jordan J Dotson¹, Lucy van Dijk¹, Jacob C Timmerman²

¹Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States.

Journal of the American Chemical Society

|December 27, 2022

Summary

This summary is machine-generated.

This study introduces a machine learning approach for optimizing catalysts with chiral bisphosphine ligands. The method successfully improved multiple reaction goals, like yield and selectivity, in pharmaceutical synthesis.

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Area of Science:

Catalysis
Organic Chemistry
Computational Chemistry

Background:

Simultaneously optimizing multiple reaction objectives (yield, enantioselectivity, regioselectivity) in catalysis is challenging.
Chiral bisphosphine ligands play a crucial role in controlling selectivity in asymmetric synthesis.

Purpose of the Study:

To develop and demonstrate a machine learning workflow for multi-objective optimization of catalytic reactions using chiral bisphosphine ligands.
To improve yield, enantioselectivity, and regioselectivity in sequential reactions for active pharmaceutical ingredient synthesis.

Main Methods:

Construction of a density functional theory-derived database of over 550 bisphosphine ligands.
Development of a designer chemical space mapping technique.
Application of classification methods to identify active catalysts and linear regression to model reaction selectivity.

Main Results:

Prediction and experimental validation of novel ligands with significantly improved performance across all reaction objectives.
Demonstrated successful optimization of two sequential reactions in asymmetric synthesis.
Identified a generalizable strategy for catalyst optimization controlled by bisphosphine ligands.

Conclusions:

The machine learning workflow provides an effective strategy for multi-objective optimization in catalysis.
This approach can be readily implemented for reactions where bisphosphine ligands dictate performance.
The findings advance the development of efficient asymmetric synthesis pathways.