In Silico Exploration of Hydroxytetrahydroxanthes as Promising Antipsychotic Candidates: A Molecular Docking, Molecular Simulation, DFT, and ADMET Study

Abstract Schizophrenia poses a substantial mental health challenge, affecting 5 individuals per thousand. The prevalence of this serious mental illness emphasizes the imperative need for ongoing research. In this study, authors have explored the therapeutic potential of a library of previously synthesized xanthene analogues by their research group as antagonists of the dopamine D2 receptor (D2R) in the context of neuropsychiatric conditions such as schizophrenia and Parkinson's disease. All the molecules were docked against the dopamine D2 receptor using Auto Dock vina. Based on the binding energy obtained, it can be seen that AR28 shows the best binding affinity (i.e., ‐11.5 kcal/mol) and also forms a stable complex. ADMET experiments assessed pharmacokinetics and safety, while computational models evaluated lipophilicity, solubility, and drug‐likeness. DFT calculations provided insights into electrical characteristics, stability, reactivity, and potential biological activities. From molecular dynamic simulations, it was found that the complexes were stable over time. For AR28 ΔG obtained from MMGBSA was found to be −82.34 kcal/mol. Our findings collectively highlight the promising inhibitory potential of these xanthene analogues against D2R activity, with the results from molecular docking, molecular simulation, ADMET investigations, and DFT simulations offering comprehensive insights into their binding interactions, pharmacokinetic parameters, and electronic properties.

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Publication Details

Journal
ChemistrySelect
Published
2025-10-01
DOI
https://doi.org/10.1002/slct.202503516
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
1.50
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article

In Silico Exploration of Hydroxytetrahydroxanthes as Promising Antipsychotic Candidates: A Molecular Docking, Molecular Simulation, DFT, and ADMET Study

Javed Khan, M. Aslam, Garima Pandey, Bhaskara Nand et al.
1 citations
ChemistrySelect
Computational Drug Discovery Methods
1.50
article

In Silico Exploration of Hydroxytetrahydroxanthes as Promising Antipsychotic Candidates: A Molecular Docking, Molecular Simulation, DFT, and ADMET Study

Javed Khan, M. Aslam, Garima Pandey, Bhaskara Nand, Vishal Singh, Anjali Rani
article en
1 citations

Abstract

Abstract Schizophrenia poses a substantial mental health challenge, affecting 5 individuals per thousand. The prevalence of this serious mental illness emphasizes the imperative need for ongoing research. In this study, authors have explored the therapeutic potential of a library of previously synthesized xanthene analogues by their research group as antagonists of the dopamine D2 receptor (D2R) in the context of neuropsychiatric conditions such as schizophrenia and Parkinson's disease. All the molecules were docked against the dopamine D2 receptor using Auto Dock vina. Based on the binding energy obtained, it can be seen that AR28 shows the best binding affinity (i.e., ‐11.5 kcal/mol) and also forms a stable complex. ADMET experiments assessed pharmacokinetics and safety, while computational models evaluated lipophilicity, solubility, and drug‐likeness. DFT calculations provided insights into electrical characteristics, stability, reactivity, and potential biological activities. From molecular dynamic simulations, it was found that the complexes were stable over time. For AR28 ΔG obtained from MMGBSA was found to be −82.34 kcal/mol. Our findings collectively highlight the promising inhibitory potential of these xanthene analogues against D2R activity, with the results from molecular docking, molecular simulation, ADMET investigations, and DFT simulations offering comprehensive insights into their binding interactions, pharmacokinetic parameters, and electronic properties.

ChemistrySelectVol. 10(38)
University of Delhi (IN), SRM Institute of Science and Technology (IN), Indira Gandhi National Open University (IN)
Openalex Percentile: Top 13%
Computational Drug Discovery Methods
1.50
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