Group-based QSAR modeling of pyrimidine derivatives for antiviral, antimalarial, and anticancer activities

Group-based quantitative structure-activity relationship (GQSAR) modeling was employed on three distinct series of twenty pyrimidine derivatives each for antiviral, antimalarial, and anticancer activities. Models were constructed based on 2D descriptors specific to individual substitution sites within the molecules (R1, R2, R3). For the antiviral series, the best model (r² = 0.923, q² = 0.783, pred_r² = 0.712, F = 59.4) incorporated descriptors R1_SLogP, R2_EState, and R3_Polarizability, indicating a strong role of hydrophobic and electronic properties. The antimalarial model (r² = 0.897, q² = 0.761, pred_r² = 0.685, F = 47.2) revealed significant influence from molecular refractivity and EState indices. The anticancer model (r² = 0.912, q² = 0.775, pred_r² = 0.695, F = 52.8) highlighted the importance of SlogP, ESI, and valence connectivity descriptors. Contribution charts and radar plots provided insights into the relative importance of descriptors, highlighting structural features critical to activity. The findings facilitate a deeper understanding of structure-activity relationships and provide a rational basis for designing improved pyrimidine-based therapeutics.

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Publication Details

Journal
World Journal of Biology Pharmacy and Health Sciences
Published
2025-06-18
DOI
https://doi.org/10.30574/wjbphs.2025.22.3.0599
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Group-based QSAR modeling of pyrimidine derivatives for antiviral, antimalarial, and anticancer activities

Sushma Somkuwar, Neelesh Chaubey
World Journal of Biology Pharmacy and Health Sciences
Computational Drug Discovery Methods
article

Group-based QSAR modeling of pyrimidine derivatives for antiviral, antimalarial, and anticancer activities

Sushma Somkuwar, Neelesh Chaubey
article en

Abstract

Group-based quantitative structure-activity relationship (GQSAR) modeling was employed on three distinct series of twenty pyrimidine derivatives each for antiviral, antimalarial, and anticancer activities. Models were constructed based on 2D descriptors specific to individual substitution sites within the molecules (R1, R2, R3). For the antiviral series, the best model (r² = 0.923, q² = 0.783, pred_r² = 0.712, F = 59.4) incorporated descriptors R1_SLogP, R2_EState, and R3_Polarizability, indicating a strong role of hydrophobic and electronic properties. The antimalarial model (r² = 0.897, q² = 0.761, pred_r² = 0.685, F = 47.2) revealed significant influence from molecular refractivity and EState indices. The anticancer model (r² = 0.912, q² = 0.775, pred_r² = 0.695, F = 52.8) highlighted the importance of SlogP, ESI, and valence connectivity descriptors. Contribution charts and radar plots provided insights into the relative importance of descriptors, highlighting structural features critical to activity. The findings facilitate a deeper understanding of structure-activity relationships and provide a rational basis for designing improved pyrimidine-based therapeutics.

World Journal of Biology Pharmacy and Health SciencesVol. 22(3)
Openalex Percentile: Top 84%
Computational Drug Discovery Methods
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Group-based QSAR modeling of pyrimidine derivatives for antiviral, antimalarial, and anticancer activities — Sushma Somkuwar, Neelesh Chaubey · World Journal of Biology Pharmacy and Health Sciences (2025) | TGRS Research Map | TGRS