The MGC803 cell line is a human gastric cancer model frequently used in cancer research. In this context, a combined in silico approach including 3D-QSAR modeling, ADMET analysis, network pharmacology, docking, molecular dynamics and ligand transport evaluations, was applied to design new antiproliferative molecules. A robust 3D-QSAR model with high predictive capacity (R² and Q²) was developed and used to design new compounds (PR1–PR4). After an ADMET screening, the putative biological targets of the non-toxic compounds were predicted using PharmMapper. A network pharmacology analysis identified several hub genes, of which HSP90AA1 had the highest degree value. Given its central role in stabilizing multiple oncogenic proteins involved in gastric cancer progression, as well as its suitability for structure-based studies, HSP90AA1 was selected for molecular docking and molecular dynamics simulations. In addition, molecular docking was performed on HSP90AA1 protein (1YET) in complex with the designed molecules (PR1-PR4), and their predicted binding behaviors were compared to both the most active molecule (M34) and the reference drug, geldanamycin. These results demonstrate a high predicted binding affinity and remarkable interaction profiles within the active site of the HSP90AA1. To further evaluate the dynamic stability of these complexes, we performed molecular dynamics simulations over a 100 ns period, thus confirming stable attachment modes and durable contact networks. The MM-PBSA approach demonstrated favorable binding free energies between the chosen PR4 ligand and the 1YET protein (-26.47 ± 2.89 kcal/mol). Finally, the ligand transport study showed that the PR4 ligand easily crosses tunnels 1 and 2 with optimal theoretical transport dynamics compared to the reference drug, geldanamycin (GA). This comprehensive computational method underlines the diverse potential of the examined molecules, identifying the most promising candidates for subsequent experimental validation against gastric cancer.
L. Naanaai, Abdellah El Aissouq, Yassine El Allouche et al.· Beni-Suef University Journal...· 0 citations
Colorectal cancer (CRC) remains a major health burden worldwide, motivating the search for safe and effective small‐molecule therapeutics. Here, we report an integrated chemoinformatics study on 34 dimedone‐derived compounds active against HT‐29 colon cancer cells. RDKit was used to compute 174 descriptors, and a wrapper selection with Hyperopt (TPE) identified a four‐descriptor multiple linear regression model (PEOE_VSA11, PEOE_VSA7, VSA_EState10, fr_NH1) that explained the biological variance well (
R
2
= 0.759; adjusted
R
2
= 0.705;
F
= 14.16,
p
= 2.16 × 10
−
5
) with minimal multicollinearity (VIF ≈ 1). Positively weighted van der Waals surface and electrotopological descriptors were the dominant drivers of potency, whereas fr_NH1 contributed slightly negatively. Guided by these trends, we designed derivatives Z7 and Z9, predicted to improve potency while maintaining reasonable developability. Molecular docking against the receptor tyrosine kinase c‐MET showed strong binding for the reference M34 (−9.3 kcal/mol) and favorable binding for Z9 (−8.0 kcal/mol) through complementary hydrophobic and electrostatic contacts. One hundred nanosecond GROMACS simulations indicated that both cMET_M34 and cMET_Z9 complexes are dynamically stable; Z9 promotes sustained one‐to‐two hydrogen bonds and a gradual reduction in solvent accessibility, consistent with deeper burial. A reinforced convergence analysis confirms that the 100 ns trajectories are well equilibrated.
Yassine El Allouche, A. Diane, Sofia El Marjany et al.· ChemistrySelect· 0 citations
Introduction: The α-amylase enzyme plays a critical role in the digestion of complex carbohydrates. Inhibiting this enzyme offers a promising strategy for improving glucose regulation in diabetic patients. Methods: In this study, a comprehensive computational approach, combining 3D-QSAR modeling, ADMET profiling, molecular docking, molecular dynamics, ligand transport analysis, and retrosynthesis, was used to identify novel ligands with potent inhibitory activity against various indenoquinoxaline-phenylacrylohydrazide hybrids. Results: The optimal 3D-QSAR model, developed using partial least squares (PLS) and Comparative Molecular Similarity Indices Analysis (CoMSIA), demonstrated strong correlation and predictive power (Q2=0.541, R2=0.973, SEE=0.076). ADMET analysis showed that the designed ligands possess acceptable pharmacokinetic and toxicological profiles, supporting their potential for further drug development. Molecular docking revealed that the designed ligands effectively interacted with the active site of α-amylase (PDB ID: 7TAA). Furthermore, molecular dynamics simulations (100 ns) and MM-PBSA free energy calculations confirmed the stability of ligand-enzyme complexes. Ligand transport was further examined using the CaverDock program, tracking the movement of molecules from the enzyme’s active site to its surface. Finally, retrosynthetic analysis was performed to propose feasible synthesis routes for the most active compound. Conclusion: Overall, the findings highlight a promising lead compound for further in vitro and in vivo investigations targeting α-amylase inhibition.
L. Naanaai, M. Alaqarbeh, Abdellah El Aissouq et al.· BioImpacts· 0 citations
Findings suggest that PR1 and PR2 are promising candidates for advanced antidiabetic drug development, exhibiting predicted enhanced inhibitory activities and favorable pharmacokinetic and toxicological profiles.
L. Naanaai, Ikram Hanout, Md. Al-Amin et al.· Journal of the Iranian Chemi...· 0 citations