Computational analysis of new generation TSPO ligands

Molecular docking, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity), and QSAR (Quantitative Structural Activity Relationships) profiling are integral components of drug discovery and development. In this study, we have utilized SeeSAR™ modules, BioSolveIT (FlexX docking) to evaluate the binding affinity of a series of 33 potential ligands for 18 kDa translocator protein (TSPO). Additionally, ADMET prediction tools, including SwissADME, ADMETlab 2.0, and ADMEai, were used to investigate the pharmacokinetic properties. Regression analysis tools were used to predict the structural activity relationships of these ligands. Out of 33 compounds, eleven molecules exhibited strong binding affinity toward TSPO (PDB ID: 4RYI), with binding free energies ranging from -65 to -50 kcal/mol. The key residues responsible for their binding were TRP A:51, GLN A:94, MET A: 3, SER A:22, PHE A:12, LYS B:37, ARG A:29, GLY A:44, LYS B:32 in the active site primarily through van der Waals forces. Other important hydrophobic interactions involving residues LEU B:60, PHE A:12, PHE A:11, PHE B:11, TYR A:15, TYR B:15, VAL B:53, LEU B:57, ILE A:8, ILE B:8. ADMET properties like logBB and Log S indicated favourable blood brain barrier permeability and appropriate aqueous solubility profiles. QSAR studies showed a good correlation between Caco 2 and BBB. In summary this study identified several promising ligands with strong binding affinity toward TSPO with favourable molecular properties, and potential for good bioavailability. Further research and experimentation are required to validate the efficacy and safety of these compounds as viable pharmaceutical candidates in drug development and exploration.

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Publication Details

Journal
Discover Molecules
Published
2025-02-03
DOI
https://doi.org/10.1007/s44345-025-00009-9
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00

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Computational analysis of new generation TSPO ligands

Anjani Kumar Tiwari, Vijay Singh, Pragati Azad
Discover Molecules
Computational Drug Discovery Methods
article

Computational analysis of new generation TSPO ligands

Anjani Kumar Tiwari, Vijay Singh, Pragati Azad
article en

Abstract

Molecular docking, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity), and QSAR (Quantitative Structural Activity Relationships) profiling are integral components of drug discovery and development. In this study, we have utilized SeeSAR™ modules, BioSolveIT (FlexX docking) to evaluate the binding affinity of a series of 33 potential ligands for 18 kDa translocator protein (TSPO). Additionally, ADMET prediction tools, including SwissADME, ADMETlab 2.0, and ADMEai, were used to investigate the pharmacokinetic properties. Regression analysis tools were used to predict the structural activity relationships of these ligands. Out of 33 compounds, eleven molecules exhibited strong binding affinity toward TSPO (PDB ID: 4RYI), with binding free energies ranging from -65 to -50 kcal/mol. The key residues responsible for their binding were TRP A:51, GLN A:94, MET A: 3, SER A:22, PHE A:12, LYS B:37, ARG A:29, GLY A:44, LYS B:32 in the active site primarily through van der Waals forces. Other important hydrophobic interactions involving residues LEU B:60, PHE A:12, PHE A:11, PHE B:11, TYR A:15, TYR B:15, VAL B:53, LEU B:57, ILE A:8, ILE B:8. ADMET properties like logBB and Log S indicated favourable blood brain barrier permeability and appropriate aqueous solubility profiles. QSAR studies showed a good correlation between Caco 2 and BBB. In summary this study identified several promising ligands with strong binding affinity toward TSPO with favourable molecular properties, and potential for good bioavailability. Further research and experimentation are required to validate the efficacy and safety of these compounds as viable pharmaceutical candidates in drug development and exploration.

Discover MoleculesVol. 2(1)
Babasaheb Bhimrao Ambedkar University (IN)
Department of Science and Technology, Ministry of Science and Technology, India
Openalex Percentile: Top 97%
Computational Drug Discovery Methods
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Computational analysis of new generation TSPO ligands — Anjani Kumar Tiwari, Vijay Singh, et al. · Discover Molecules (2025) | TGRS Research Map | TGRS