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

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.

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.

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

An integrated computational strategy involving pharmacophore mapping, molecular docking, molecular dynamics simulations, and density functional theory (DFT) analysis was employed to identify potential SaFtsZ inhibitors, highlighting compound 15 as a computationally predicted scaffold for the development of SaFtsZ-targeted antibacterial agents.

Computational structure-based screening identifies potential inhibitors of TRIM33α

The identified compounds may serve as valuable starting points for further experimental validation and structural optimization toward the development of novel anticancer agents targeting TRIM33, and establishes a robust computational framework for developing TRIM33α-targeted therapies.

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

In silico identification of novel PTP1B inhibitors by the investigation of molecular mechanism, QSAR, and DFT studies

Protein tyrosine phosphatase 1B (PTP1B) is a well-known and promising drug target involved in the negative regulation of insulin and leptin signaling, and new anti-diabetic molecules for the treatment of type 2 diabetes are directly in its hands. A series of PTP1B inhibitors was discovered and characterized using in silico techniques, including virtual screening, molecular docking, ADMET profiling, toxicity prediction, VEGA-QSAR analysis, molecular dynamics (MD) simulation, and density functional theory (DFT) calculations. A starting library of 1000 anti-diabetic compounds was screened, and PubChem CID 44560696 was identified as the most successful hit. The most potent inhibitor was then identified as CID 44560744 through chemical analogy-based refinement, with a binding free energy of − 9.13 kcal/mol and the ability to form several stabilizing hydrogen bonds and hydrophobic interactions with the key catalytic residues. DFT-optimized geometry and docking revealed that the lead compound shows high stability and strong PTP1B inhibition, showing superior binding affinity compared to ursolic acid, which exhibited a lower binding energy of − 6.34 kcal mol−1. The MD simulations showed that the PTP1B-ligand complex was structurally stable and compacted during the 200 ns simulation, with desirable profiles for RMSD, RMSF, Rg, SASA, and hydrogen bonds. The DFT and FMO analyses indicated that the compound exhibits extreme chemical reactivity and electron-transfer capability, owing to its low HOMO-LUMO gap, high softness, and moderate electrophilicity. The results of ADMET and toxicity evaluations (SwissADME, ProTox-II, and VEGA-QSAR) pointed to satisfactory solubility, non-carcinogenicity, no mutagenicity, and high pharmacological safety. Overall, the synergy of the computational methods has made CID 44560744 a lead scaffold for the development and optimization of selective PTP1B inhibitors for T2DM therapy.