Issue 8, 2023

High-throughput computational workflow for ligand discovery in catalysis with the CSD

Abstract

A novel semi-automated, high-throughput computational workflow for ligand/catalyst discovery based on the Cambridge Structural Database is reported. Two potential transition states of the Ullmann–Goldberg reaction were identified and used as a template for a ligand search within the CSD, leading to >32 000 potential ligands. The ΔG for catalysts using these ligands were calculated using B97-3c//GFN2-xTB with high success rates and good correlation compared to DLPNO-CCSD(T)/def2-TZVPP. Furthermore, machine learning models were developed based on the generated data, leading to accurate predictions of ΔG, with 70.6–81.5% of predictions falling within ± 4 kcal mol−1 of the calculated ΔG, without the need for the costly calculation of the transition state. This accuracy of machine learning models was improved to 75.4–87.8% using descriptors derived from TPSS/def2-TZVP//GFN2-xTB calculations with a minimal increase in computational time. This new workflow offers significant advantages over currently used methods due to its faster speed and lower computational cost, coupled with excellent accuracy compared to higher-level methods.

Graphical abstract: High-throughput computational workflow for ligand discovery in catalysis with the CSD

Supplementary files

Article information

Article type
Paper
Submitted
16 Jan 2023
Accepted
20 Mar 2023
First published
22 Mar 2023
This article is Open Access
Creative Commons BY license

Catal. Sci. Technol., 2023,13, 2407-2420

High-throughput computational workflow for ligand discovery in catalysis with the CSD

M. A. S. Short, C. A. Tovee, C. E. Willans and B. N. Nguyen, Catal. Sci. Technol., 2023, 13, 2407 DOI: 10.1039/D3CY00083D

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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