Browsing by Author "Mayank Garhewal"

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    2D-QSAR Modeling of Quinazoline Derivatives as EGFR Inhibitors for Colorectal Cancer
    (Chitkara University Publications, 2026-08-13) Rishab Pathak; Mayank Garhewal; Shani Yadav; Jyoti Prakash Panda; Achal Mishra; Ayush Tiwari; Avantika Agrawal; Shekhar Verma; Yogesh Vaishnav
    Background: Globally, colorectal cancer represents one of the most common types of cancer, along with being one of the top 10 causes of cancer-related deaths. Epidermal Growth Factor Receptors (EGFRs) are a major target for colorectal cancer, and quinazoline derivatives have shown anticancer activity as EGFR inhibitors. Purpose: The objective of this research was the development of a two-dimensional quantitative structure–activity relationship model (2D-QSAR); that is, to develop a simple way to identify the structural components that help predict a potent compound with the highest predicted activity among the series of quinazoline derivatives for EGFR inhibitors for colorectal cancer. Methods: A dataset of 21 quinazoline derivatives was collected from the literature, molecular descriptors were calculated, and Partial Least Squares (PLS) regression was used to develop statistically significant QSAR models. Generated and validated using internal and external validation parameters. Results: Model-1 (RANDOM_70_30_SFB_PLS_TRIALS_2) showed the best statistical performance with r² = 0.6538, q² = 0.5314, pred-r² = 0.6940, and an F-test value of 22.6638. The descriptors SsssNcount and SaaCHE-index were identified as significant contributors to EGFR inhibitory activity, indicating that tertiary nitrogen count and charge distribution play crucial roles in determining biological potency. Conclusion: The developed 2D-QSAR model identified a potent compound with the highest predicted biological activity as an EGFR inhibitor for colorectal cancer.