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

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.

Structure-based discovery of novel ROCK2 inhibitors using integrated virtual screening, molecular dynamics, metadynamics and absolute binding free energy calculations

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.

Comparative Structure-Based Molecular Docking Analysis of Clinically Approved CDK4/6 Inhibitors Targeting CDK6

Comparison in silico analysis provides structural insights into the binding behavior of clinical CDK4/6 inhibitors and may contribute to future optimization and development of CDK-targeted therapies.

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.