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Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity. | LitMetric

Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity.

ACS Catal

Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.

Published: June 2024

AI Article Synopsis

  • - Chiral ligands are crucial for asymmetric homogeneous catalysis, but traditional methods for their synthesis and screening can be lengthy and resource-heavy.
  • - The paper presents a new data-driven approach that uses a combination of feature selection and Bayesian regression to optimize ligands more quickly and efficiently, applicable to various reactions.
  • - The researchers developed the BDL-Cu-2023 dataset with 312 bidentate ligands and successfully used their method to identify potential candidates for a complex asymmetric reaction.

Article Abstract

Chiral ligands are important components in asymmetric homogeneous catalysis, but their synthesis and screening can be both time-consuming and resource-intensive. Data-driven approaches, in contrast to screening procedures based on intuition, have the potential to reduce the time and resources needed for reaction optimization by more rapidly identifying an ideal catalyst. These approaches, however, are often nontransferable and cannot be applied across different reactions. To overcome this drawback, we introduce a general featurization strategy for bidentate ligands that is coupled with an automated feature selection pipeline and Bayesian ridge regression to perform multivariate linear regression modeling. This approach, which is applicable to any reaction, incorporates electronic, steric, and topological features (rigidity/flexibility, branching, geometry, and constitution) and is well-suited for early stage ligand optimization. Using only small data sets, our workflow capably predicts the enantioselectivity of four metal-catalyzed asymmetric reactions. Uncertainty estimates provided by Bayesian ridge regression permit the use of Bayesian optimization to efficiently explore pools of prospective ligands. Finally, we constructed the BDL-Cu-2023 data set, composed of 312 bidentate ligands extracted from the Cambridge Structural Database, and screened it with this procedure to identify ligand candidates for a challenging asymmetric oxy-alkynylation reaction.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11197013PMC
http://dx.doi.org/10.1021/acscatal.4c02452DOI Listing

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