2D and 3D-QSAR Modeling of Hydrazone Derivatives as Antiplatelet Agents
Iranian Journal of Pharmaceutical Sciences,
Vol. 22 No. 1 (2026),
26 January 2026
,
Page 362-375
https://doi.org/10.22037/ijps.v22i1.51576
Abstract
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, with platelet aggregation playing a key role in thrombus formation. Hydrazone derivatives have emerged as promising antiplatelet agents, yet a systematic understanding of their structure–activity relationships remains limited. In this study, we developed predictive 2D and 3D QSAR models for a series of hydrazone derivatives as inhibitors of arachidonic acid-induced platelet aggregation.
In 2D-QSAR analysis, the stepwise multiple linear regression (SW-MLR) and genetic algorithm-based MLR (GA-MLR) models exhibited satisfactory predictive accuracy for both the training and test sets. Notably, the GA-MLR model showed better external predictivity, with an R²test value of 0.81, compared to the SW-MLR model.
Based on the 3D-QSAR analysis, the CoMFA model showed better performance in predicting pIC₅₀ values (R²train = 0.920, Q2LOO = 0.531, and R²test = 0.677) than the CoMSIA model (R²train = 0.890, Q2LOO = 0.502, and R²test = 0.600). Furthermore, 3D-QSAR studies using CoMFA and CoMSIA further revealed that steric, electrostatic, hydrophobic, and hydrogen-bond donor/acceptor fields influence antiplatelet activity. Overall, the 2D and 3D QSAR models provide a reliable predictive framework for the antiplatelet potency of hydrazone derivatives and can guide the design of more potent and selective inhibitors, supporting future cardiovascular drug discovery.
- Hydrazone derivatives
- Platelet aggregation
- QSAR
- CoMFA
- CoMSIA
- cardiovascular disease
How to Cite
References
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