Aug 2026· Biointerface Research in Applied Chemistry· 0 citations· 32 references
TL;DR
Computational findings support the prioritization of X14 for further experimental validation in glioblastoma therapy, and generally favorable ADMET profiles were observed, hepatotoxicity alerts were predicted for all compounds, which represents an important limitation supporting the prioritization of X14.
Abstract
A 2D quantitative structure–activity relationship (QSAR) model was developed for a series of 34 dihydropteridine derivatives to predict their antitumor activity against glioblastoma. The multiple linear regression model (MLR), based on four descriptors (ATS7s, AATS8e, AATS3p, and AATS5p), demonstrated satisfactory statistical performance (R² = 0.714, R²_adj = 0.660, Q² = 0.513, RMSE = 0.096, F = 13.134, p < 0.0001), with strong external validation (R²_test = 0645). Model-guided optimization led to the design and screening of new structural analogs, which were subsequently docked against Polo-like kinase 1 (PLK1, PDB ID: 3BD6), a key mitotic regulator overexpressed in glioblastoma. Among the compounds evaluated, X14 exhibited the most favorable docking affinity (-10.5 kcal / mol), compared to -6.6 kcal/mol for Temozolomide (TMZ) and 8.1 kcal/mol for the reference compound N27. Although generally favorable ADMET profiles were observed, hepatotoxicity alerts were predicted for all compounds, which represents an important limitation supporting the prioritization of X14. In general, this study provides. Molecular dynamics simulations over 100 ns supported stable complex formation, with RMSD backbone values stabilizing around 2.5 to 3.0 Å. The ligands remained bound within the binding pocket throughout the simulation, exhibiting RMSD values below 1.5 Å for X14 and temozolomide and below 2.5 Å for N27, while the PLK1–TMZ complex showed higher structural fluctuations. A plausible synthetic route was proposed to assess the experimental feasibility of X14. Overall, these computational findings support the prioritization of X14 for further experimental validation in glioblastoma therapy.
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
Caspase-1 is a crucial inflammatory cysteine protease that facilitates the maturation of pro-inflammatory cytokines such as interleukin-1β and interleukin-18, making it a significant therapeutic target for inflammatory diseases. However, existing caspase-1 inhibitors often face challenges like toxicity and suboptimal drug-like properties, underscoring the need for new inhibitors. This study employed an integrated computational strategy, combining quantitative structure–activity relationship (QSAR) modeling and application of this validated model to a large natural product database followed by molecular docking, rigorous binding free energy analysis and extended molecular dynamics simulations. Initially, a dataset of 185 caspase-1 inhibitors with experimentally reported pKi values (ranging from 4.05 to 9.24) was used to construct a QSAR model using Partial Least Squares (PLS) regression. The PLS-based QSAR model was developed with 18 descriptors out of 5799 calculated descriptors for each compound and 10 latent variables, demonstrating strong statistical performance with R2 values of 0.870 and 0.838 for the training and test sets, respectively, and leave-one-out cross-validation coefficient Q2LOO and 5-fold cross-validation (Q25-fold) values of 0.819 and 0.814, respectively. Y-randomization tests further confirmed the model’s robustness, as the randomized models exhibited significantly lower statistical parameters than the original model. The validated QSAR model was applied to 276,518 natural products in the LOTUS database. Subsequent molecular docking, Molecular Mechanics/General Born Surface Area (MM/GBSA) scoring, and Pan-Assay INterference Compounds (PAINS) and Chemical Frequent Hitter (ChemFH) filtering identified 14 candidate compounds, which were further evaluated using 300 ns molecular dynamics simulations. Among these, four natural products (LTS0162325, LTS0221286, LTS0016840, and LTS0070407) showed the most stable binding behavior and maintained persistent interactions with key catalytic and substrate-binding residues of caspase-1 in a mimicked physiological condition. Overall, this study highlights natural diterpenoids and coumarin glycosides as promising scaffolds for caspase-1 inhibition and demonstrates that integrating QSAR modeling with structure-based approaches provides an efficient strategy for discovering potential anti-inflammatory drug candidates.
Yusuf Şeflekçi, Alper Yılmaz, Abdulilah Ece· Molecules· 0 citations
The findings from molecular docking, 500-ns molecular dynamics simulations, MM-GBSA calculations, and alanine scanning analyses collectively corroborate a stable binding mode of BTB11556 within the MPO active site, support further investigation of BTB11556 as a candidate compound associated with MPO-targeted therapeutic strategies.
Maysoon Raed Alnajdawi, H. Wahab, Belal Alnajjar et al.· Journal of Computer-Aided Mo...· 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
This study examines seventy‐one 1,4‐naphthoquinone scaffold‐bearing compounds with known half‐maximal inhibitory concentration (IC
50
) against
Mycobacterium tuberculosis
(Mtb) using QSAR, docking, and molecular dynamics (MD) simulations. The molecular descriptors were computed using PaDEL and ChemDes to create multiple linear regression (MLR) based predictive 2D QSAR models through QSARINS v2.2.4. The statistically suitable five‐descriptor QSAR model demonstrated a correlation coefficient (
R
2
0.7136) and a cross‐validated
R
2
(
Q
2
LOO
0.6599). The model exhibited lower values for root mean squared error (RMSE
tr
0.2298) and mean absolute error (MAE
tr
0.1738), along with a higher concordance correlation coefficient (CCC 0.8329), indicating strong fitness and predictive accuracy. In silico screening of all compounds for physicochemical and medicinal chemistry parameters, followed by docking against five key Tb pathogenesis proteins using Cresset Flare 10.0.1, identified 24 leading candidates. A 200 ns MD simulation revealed good protein‐ligand complex stability of two compounds,
52
and
70
, which was further supported by MM/GBSA calculation. SAR analysis demonstrates that introducing chlorine into the quinone scaffold, in combination with highly lipophilic aryl substituents such as trifluoromethyl, significantly enhances binding affinity. Considering suitable druggability parameters, we suggest compound
70
for further research to confirm its potential as an effective anti‐TB drug.
Pallavi Singh, Harish C. Upadhyay, Somya Maurya· ChemistrySelect· 0 citations
Breast cancer is characterised by the uncontrolled growth of cells within
the mammary glands, which can later spread to adjacent tissues. The current treatment options include alkylating agents, intercalating agents, topoisomerase inhibitors, antimetabolites, and antimitotic drugs. Computer-aided drug design has contributed in a large way to the development of anticancer drugs. The objective of this study was to perform pharmacophore optimisation of 4-
aminoquinoline derivatives using QSARINS software for the development of QSAR models for anticancer activity.
A series of thirty-six 4-aminoquinoline derivatives was used to generate 2D and fingerprint-based QSAR models using the Genetic Algorithm-Multiple Linear Regression (GA-MLR)
method. The resulting statistical parameters and graphical data were evaluated, and models with
strong statistical performance were selected for further analysis.
The 2D QSAR model exhibited R² = 0.9757 and Q² = 0.9490, and the fingerprint-based
QSAR model displayed values of R² = 0.9884 and Q² = 0.9736. Both models showed internal robustness with limited external predictive capability.
The results indicate that electronic and steric factors around the 4-aminoquinoline nucleus strongly influence anticancer activity. The optimised pharmacophore provides a framework
for designing new, more potent anticancer agents.
The optimised pharmacophore indicates the contribution of electronegative substituents at the 7th position of the quinoline ring, along with a dimethylamino biphenyl group at the 3rd
position, for better anticancer activity. Considering the limitations of the present study, namely, limited external predictive capability, further refinement of the models would be required.
S. Patil, K. Asgaonkar, Darshani Gholap et al.· Current Enzyme Inhibition· 0 citations