Chemical space exploration with Molpher: Generating and assessing a glucocorticoid receptor ligand library

Computational exploration of chemical space is crucial in modern cheminformatics research for accelerating the discovery of new biologically active compounds. In this study, we present a detailed analysis of the chemical library of potential glucocorticoid receptor (GR) ligands generated by the mole...

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Bibliographic Details
Published in:Molecular informatics 2024-08, Vol.43 (8), p.e202300316-n/a
Main Authors: Agea, M. Isabel, Čmelo, Ivan, Dehaen, Wim, Chen, Ya, Kirchmair, Johannes, Sedlák, David, Bartůněk, Petr, Šícho, Martin, Svozil, Daniel
Format: Article
Language:English
Subjects:
Bioactive compounds
Biological activity
Biological models (mathematics)
Cell culture
Chemical activity
Chemical composition
Chemical compounds
chemical space
de novo design
glucocorticoid receptor
Glucocorticoid receptors
Glucocorticoids
Informatics
Ligands
molecular generation
Receptors
Space exploration
Workflow
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Summary:Computational exploration of chemical space is crucial in modern cheminformatics research for accelerating the discovery of new biologically active compounds. In this study, we present a detailed analysis of the chemical library of potential glucocorticoid receptor (GR) ligands generated by the molecular generator, Molpher. To generate the targeted GR library and construct the classification models, structures from the ChEMBL database as well as from the internal IMG library, which was experimentally screened for biological activity in the primary luciferase reporter cell assay, were utilized. The composition of the targeted GR ligand library was compared with a reference library that randomly samples chemical space. A random forest model was used to determine the biological activity of ligands, incorporating its applicability domain using conformal prediction. It was demonstrated that the GR library is significantly enriched with GR ligands compared to the random library. Furthermore, a prospective analysis demonstrated that Molpher successfully designed compounds, which were subsequently experimentally confirmed to be active on the GR. A collection of 34 potential new GR ligands was also identified. Moreover, an important contribution of this study is the establishment of a comprehensive workflow for evaluating computationally generated ligands, particularly those with potential activity against targets that are challenging to dock. In this study, it was demonstrated that the molecular generator, Molpher, significantly enriches the chemical library with glucocorticoid receptor (GR) ligands compared to a random library. Utilizing the ChEMBL and IMG internal library of bioactive compounds, 34 potential new GR ligands were identified, and a comprehensive workflow for evaluating computationally generated ligands was established.
ISSN:1868-1743
1868-1751
1868-1751
DOI:10.1002/minf.202300316