Pharmacophore-based virtual screening, molecular docking, MD simulation, MM-GBSA energy calculation and DFT analysis for the in silico discovery of a SaFtsZ inhibitor

Pharmacophore-based identification and molecular docking of CCR5 inhibitors: insights from dynamic simulations

The integrated computational approach identified ZINC000000867238 as a potent and stable CCR5 inhibitor candidate, warranting further in vitro and in vivo validation as a potential HIV-1 entry blocker.

Identification of Potential SARS-CoV-2 Main Protease (MPro) Inhibitors Through Pharmacophore Modeling, Molecular Docking, and Molecular Dynamics Simulation Approaches

The main protease (MPro) of coronaviruses (CoVs) is an essential enzyme involved in viral replication and represents an attractive target for antiviral drug discovery. Based on the similar binding pocket residues within the MPro of different CoVs, this study aimed to identify potential inhibitors of SARS-CoV-2 MPro from PDB ID 6M2N using integrated computational approaches. Interaction-based pharmacophore modeling, virtual screening, molecular docking, MM-GBSA binding energy calculation, and molecular dynamics simulation (MDS) were performed using BIOVIA Discovery Studio. The validated pharmacophore model was utilized to screen the ZINC database, followed by docking and 100 ns MDS analyses of the top-ranked compounds. The pharmacophore model 01 demonstrated favorable predictive performance (AUC = 0.781). Virtual screening identified 483 compounds, from which 15 compounds were selected for docking studies. Among them, ZINC95473654 (Lig-1), ZINC95473725 (Lig-2), and ZINC08792368 (Lig-3) exhibited strong binding affinity toward MPro. Lig-1 demonstrated the best docking score and binding free energy, along with stable interactions with key catalytic residues HIS41, CYS145, and GLU166. MDS analyses further confirmed that Lig-1, Lig-2 and Lig-3 maintained stable conformations. The hydrogen bond distance monitoring and post MDS-MM-GBSA results suggest Lig-1 followed by Lig-3 as an inhibitor for MPro and persistent intermolecular interactions throughout the 100 ns simulation period. The findings suggest that Lig-1, followed by Lig-3, may serve as promising computational lead compounds targeting SARS-CoV-2 MPro, representing promising candidates for further experimental validation.

Discovery of Potent CDK2 Inhibitors Through Structure-Based Virtual Screening and MD Simulation Studies.

Overall, MD1-MD5 demonstrated excellent binding, structural stability, and pharmacokinetic properties, making them strong candidates for future CDK2-targeted anticancer research.

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

Integrated molecular docking, toxicity prediction, and molecular dynamics simulations studies to identify potential antimalarial candidates targeting enoyl-acyl carrier protein reductase enzyme

A cost-effective computational workflow for prioritizing ENR inhibitors is demonstrated, providing a foundation for the development of novel antimalarial agents and highlighting Cd3 and Cd5 as the most promising candidates for further development.