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August 9, 2026Scientific ReportsOpen Access

QSPR modeling of physicochemical properties of SSRI and SNRI antidepressants using advanced molecular graph descriptors

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Authors

AKAqsa KabeerUniversity of LahoreZMZeeshan Saleem MuftiUniversity of LahoreAAAbdulrahman A. AlmehiziaKing Saud University

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Implication

Randomized trial models physicochemical properties in antidepressants, suggesting efficient predictive methods.

Key Points

  • The aim is to develop accurate QSPR models for predicting physicochemical properties of antidepressants.
  • Utilized 25 antidepressant drugs for QSPR analysis with topological indices from chemical graph theory.
  • Calculated 9 descriptors and analyzed their correlations with 8 physicochemical properties.
  • Developed and assessed regression models (linear, quadratic, logarithmic) using statistical metrics.
  • Quadratic regression model for boiling point showed a maximum R² value of 0.887 with ND_1(G) descriptor.
  • ND_1(G) and ND_5(G) emerged as key descriptors for various physicochemical properties.
  • The QSPR models are reliable, requiring only molecular topology for predictions and minimizing experimental needs.

Cite This Study

Kabeer et al. (2026) studied this question.

synapsesocial.com/papers/6a782d862e1896536c840afehttps://doi.org/10.1038/s41598-026-65586-2
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Also Consider

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  5. 5Advancing QSPR with sum-connectivity descriptors: physicochemical and antibacterial modelling via molecular graph connectivity2026