QSAR, molecular dynamics, and biological evaluation of novel myeloperoxidase inhibitors via ligand-based pharmacophore modeling as potential anticancer agents

A Comprehensive Study Utilizing QSAR, Virtual Screening, Molecular Docking, Molecular Dynamics, and MM/GBSA Analyses Reveals Natural Diterpenoids as Promising Caspase-1 Inhibitors

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.

Computational identification of potential VEGFR2 inhibitors using integrated pharmacophore modeling, QSAR analysis, molecular docking, dynamics simulation, and virtual screening followed by in vitro bioassays

Molecular Docking and Pharmacological In Silico Evaluation of Genistein and Hematoxylin as Promising Ribonucleotide Reductase-targeted Therapies for Cancer.

Genistein and hematoxylin demonstrate promising molecular interactions and pharmacological profiles as potential natural RR inhibitors and supports further preclinical development as anticancer agents.

QSAR-Based Pharmacophore Optimization of 4-Aminoquinoline Derivatives: Computational Insights into Structure-Activity Relationship and Anticancer Potential

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.

In Silico Optimization of Dihydropteridine Derivatives Targeting PLK1 for Glioblastoma: An Integrated QSAR, Docking, and Molecular Dynamics Study

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.

A Comprehensive Computational Investigation of Novel Hydrazide–Hydrazone Derivatives as Potential EGFR Inhibitors for Gastric Cancer Treatment

Findings support the Hydrazide–hydrazone derivatives' potential as EGFR kinase inhibitors, with M3 emerging as a particularly promising lead compound.