ChemSpaX : exploration of chemical space by automated functionalization of molecular scaffold

ChemSpaX : exploration of chemical space by automated functionalization of molecular scaffold. Kalikadien, A. V., Pidko, E. A., & Sinha, V. Digital Discovery, 1(1):8–25, 2022.

Paper doi abstract bibtex

This work introduces ChemSpaX , an open-source Python-based tool for automated exploration of chemical space of molecular scaffolds with a special focus on transition-metal complexes. , Exploration of the local chemical space of molecular scaffolds by post-functionalization (PF) is a promising route to discover novel molecules with desired structure and function. PF with rationally chosen substituents based on known electronic and steric properties is a commonly used experimental and computational strategy in screening, design and optimization of catalytic scaffolds. Automated generation of reasonably accurate geometric representations of post-functionalized molecular scaffolds is highly desirable for data-driven applications. However, automated PF of transition metal (TM) complexes remains challenging. In this work a Python-based workflow, ChemSpaX , that is aimed at automating the PF of a given molecular scaffold with special emphasis on TM complexes, is introduced. In three representative applications of ChemSpaX by comparing with DFT and DFT-B calculations, we show that the generated structures have a reasonable quality for use in computational screening applications. Furthermore, we show that ChemSpaX generated geometries can be used in machine learning applications to accurately predict DFT computed HOMO–LUMO gaps for transition metal complexes. ChemSpaX is open-source and aims to bolster and democratize the efforts of the scientific community towards data-driven chemical discovery.

@article{kalikadien_chemspax_2022,
	title = {\textit{{ChemSpaX}} : exploration of chemical space by automated functionalization of molecular scaffold},
	volume = {1},
	issn = {2635-098X},
	shorttitle = {\textit{{ChemSpaX}}},
	url = {http://xlink.rsc.org/?DOI=D1DD00017A},
	doi = {10.1039/D1DD00017A},
	abstract = {This work introduces
              ChemSpaX
              , an open-source Python-based tool for automated exploration of chemical space of molecular scaffolds with a special focus on transition-metal complexes.
            
          , 
            
              Exploration of the local chemical space of molecular scaffolds by post-functionalization (PF) is a promising route to discover novel molecules with desired structure and function. PF with rationally chosen substituents based on known electronic and steric properties is a commonly used experimental and computational strategy in screening, design and optimization of catalytic scaffolds. Automated generation of reasonably accurate geometric representations of post-functionalized molecular scaffolds is highly desirable for data-driven applications. However, automated PF of transition metal (TM) complexes remains challenging. In this work a Python-based workflow,
              ChemSpaX
              , that is aimed at automating the PF of a given molecular scaffold with special emphasis on TM complexes, is introduced. In three representative applications of
              ChemSpaX
              by comparing with DFT and DFT-B calculations, we show that the generated structures have a reasonable quality for use in computational screening applications. Furthermore, we show that
              ChemSpaX
              generated geometries can be used in machine learning applications to accurately predict DFT computed HOMO–LUMO gaps for transition metal complexes.
              ChemSpaX
              is open-source and aims to bolster and democratize the efforts of the scientific community towards data-driven chemical discovery.},
	language = {en},
	number = {1},
	urldate = {2022-06-02},
	journal = {Digital Discovery},
	author = {Kalikadien, Adarsh V. and Pidko, Evgeny A. and Sinha, Vivek},
	year = {2022},
	pages = {8--25},
}

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PF with rationally chosen substituents based on known electronic and steric properties is a commonly used experimental and computational strategy in screening, design and optimization of catalytic scaffolds. Automated generation of reasonably accurate geometric representations of post-functionalized molecular scaffolds is highly desirable for data-driven applications. However, automated PF of transition metal (TM) complexes remains challenging. In this work a Python-based workflow, ChemSpaX , that is aimed at automating the PF of a given molecular scaffold with special emphasis on TM complexes, is introduced. In three representative applications of ChemSpaX by comparing with DFT and DFT-B calculations, we show that the generated structures have a reasonable quality for use in computational screening applications. 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