In silico exploration of novel EGFR-targeting compounds: integrative molecular modeling, docking, pharmacokinetics, and MD simulations for advancing anti-cervical cancer therapeutics

Abstract

Cervical cancer continues to pose a significant health challenge, especially in resource-limited settings, highlighting the need for the development of novel therapeutic agents. This study investigates the potential of 2,4-diphenyl indenol [1,2-b] pyridinol derivatives as inhibitors targeting the epidermal growth factor receptor (EGFR) through computational drug discovery methods. A genetic algorithm-multiple linear regression (GA-MLR) model was created, achieving strong predictive accuracy with R² = 0.9243, Q² = 0.8957, CCC = 0.9021, and MAE = 0.034. Molecular docking studies indicated that ligand 57 displayed the highest binding affinity of -29.2313 kcal/mol, followed by ligands 111 (-29.1459 kcal/mol) and 110 (-29.9082 kcal/mol), all of which stabilize key EGFR residues. Molecular dynamics (MD) simulations confirmed the stability of ligand 111, showing an improved binding free energy of -18.2235 kcal/mol. Additionally, pharmacokinetic analysis further validated their favorable ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, supporting their potential as drug-like candidates. These findings establish a strong foundation for the development of EGFR-targeted therapies for cervical cancer.

Keywords: Cervical cancer; Docking; Modeling; QSAR; Simulation dynamics; VIF; Y-randomization.

Fig. 1

Graph of residual against descriptor.

Fig. 2

Graph of residual against descriptor.

Fig. 3

Residual plot against SpMax3 descriptor.

Fig. 4

2D and 3D crystals receptor and ligand 57 interaction.

Fig. 5

2D and 3D crystals receptor and ligand 110 interaction.

Fig. 6

2D and 3D crystal receptor and 111 ligand interaction.

Fig. 7

Dynamic simulation of protein ligand complex.