ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold

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08 June 2021, Version 2

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Working Paper

This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Local chemical space exploration of an experimentally synthesized material can be done by making slight structural

variations of the synthesized material. This generation of many molecular structures with reasonable quality,

that resemble an existing (chemical) purposeful material, is needed for high-throughput screening purposes in

material design. Large databases of geometry and chemical properties of transition metal complexes are not

readily available, although these complexes are widely used in homogeneous catalysis. A Python-based workflow,

ChemSpaX, that is aimed at automating local chemical space exploration for any type of molecule, is introduced.

The overall computational workflow of ChemSpaX is explained in more detail. ChemSpaX uses 3D information,

to place functional groups on an input structure. For example, the input structure can be a catalyst for which one

wants to use high-throughput screening to investigate if the catalytic activity can be improved. The newly placed

substituents are optimized using a computationally cheap force-field optimization method. After placement of

new substituents, higher level optimizations using xTB or DFT instead of force-field optimization are also possible

in the current workflow. In representative applications of ChemSpaX, it is shown that the structures generated by

ChemSpaX have a reasonable quality for usage in high-throughput screening applications. Representative applications

of ChemSpaX are shown by investigating various adducts on functionalized Mn-based pincer complexes,

hydrogenation of Ru-based pincer complexes, functionalization of cobalt porphyrin complexes and functionalization

of a bipyridyl functionalized cobalt-porphyrin trapped in a M2L4 type cage complex. Descriptors such as

the Gibbs free energy of reaction and HOMO-LUMO gap, that can be used in data-driven design and discovery

of catalysts, were selected and studied in more detail for the selected use cases. The relatively fast GFN2-xTB

method was used to calculate these descriptors and a comparison was done against DFT calculated descriptors.

ChemSpaX is open-source and aims to bolster the efforts of the scientific community towards data-driven material

discovery.

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