Integrated structure-based and fragment-guided design of isonicotinic acid derivatives as potential KDM4A inhibitors: an insilico study

Structure-based discovery of diphenylpyrazines as disruptors of the Skp2–Cks1 protein–protein interaction: computational insights and biochemical validation

In silico evaluation of novel metabolites from Tricholoma pardinum: Insights into drug-likeness, toxicity and target interactions via molecular docking and dynamics.

Natural products from fungi are a significant source for drug discovery. This study investigates six previously uncharacterized metabolites isolated from the poisonous mushroom Tricholoma pardinum using an integrated in silico approach to evaluate their therapeutic potential. Pharmacokinetic (ADMET) profiling predicted varied drug-likeness and toxicity profiles, with several compounds showing potential to cross the blood-brain barrier. Molecular docking and MM-GBSA calculations identified compound 1 as a potent inhibitor of Poly [ADP-ribose] polymerase 1 (PARP1), with a strong binding affinity (XP GScore: -6.756; ΔGbind: -48.75 kcal/mol). This interaction is anchored by a robust network of hydrogen bonds with key residues, including ASP914, CYS908, and THR866. Similarly, compound 6 emerged as a strong binder to Phosphatidylinositol 5-phosphate 4-kinase type-2 gamma (PIP4K2γ) (XP GScore: -7.705; ΔGbind: -45.94 kcal/mol), with its binding stabilized by extensive hydrophobic interactions complemented by a critical hydrogen bond with the residue Methionine 206 (MET206). Subsequent 100 ns molecular dynamics simulations confirmed the high stability of both protein-ligand complexes, validating the persistence of these key interactions. These computational findings highlight the potential of metabolites from T. pardinum as novel scaffolds for developing anticancer agents targeting PARP1 and PIP4K2γ, warranting further experimental validation.

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

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.

Molecular Docking and ADME Profiling of N-(Substituted-1,3-benzothiazol-2-yl)benzamide Derivatives as Potential Anticonvulsant Agents

Amides represent a class of organic compounds with a variety of biological potential and anticonvulsant properties.This study aimed to evaluate the pharmacokinetic properties and molecular interactions of 15 N-(substituted-1,3-benzothiazol-2-yl)benzamide derivatives with two key epilepsy-related targets, γ-aminobutyric acid aminotransferase (GABA-AT) and activated open sodium ion channel proteins, in order to assess their potential anticonvulsant activity. Crystal structures of GABA-AT (PDB ID: 1OHW) and the sodium ion channel (PDB ID: 5HVX) were obtained from the RCSB Protein Data Bank. Molecular docking was performed using AutoDock Vina following protein preparation in Chimera v1.11.2. Post-docking analysis was conducted using Chimera and Discovery Studio Visualizer. ADME properties were also predicted. ADME analysis showed high gastrointestinal absorption for all compounds except compound 5. Docking results revealed that seven compounds (Cp1, Cp3, Cp7, Cp10, Cp12, Cp13, and Cp14) exhibited interaction profiles similar to vigabatrin, while three (Cp10, Cp13, and Cp14) aligned with lamotrigine. These compounds demonstrated favorable binding interactions with both targets. The docking analysis suggest that selected N-(substituted-1,3-benzothiazol-2-yl)benzamide derivatives, particularly Cp1, Cp3, Cp7, Cp10, Cp12, Cp13, and Cp14 were identified as the most promising lead candidates, with significant potential for anticonvulsant activity.

Design of novel derivatives of 1,3,4-thiadiazole against the α-amylase enzyme using 3D-QSAR, ADMET evaluation, docking analysis, molecular dynamic simulations and MM-PBSA approaches

Design and Evaluation of Naphthoxy and Phenoxy amide as Potential PARP 1 inhibitors: Molecular Docking and In Vitro Analysis.

INTRODUCTION The objective was to design, synthesize, and evaluate novel naphthoxy and phenoxy amide derivatives as potential poly(ADP-ribose) polymerase-1 (PARP1) inhibitors, aiming to identify compounds with improved binding affinity, favorable pharmacokinetic properties, and enhanced anticancer activity compared with existing PARP1 inhibitors. METHODS A series of naphthoxy and phenoxy amide derivatives (A1-A9 and B1-B9) were evaluated using combined computational and experimental approaches. Molecular docking against PARP1 (PDB ID: 4ZZZ) was performed using Glide to assess binding affinity. ADMET and drug-likeness properties were predicted via SWISS-ADME, and binding free energies were refined using Prime MM-GB/SA. The lead compound B2 underwent a 50-ns molecular dynamics simulation using Desmond. In vitro cytotoxicity was assessed against MCF-7 human breast cancer cell lines. RESULTS Compounds B2 and B3 exhibited strong docking scores comparable to the reference PARP1 inhibitor and demonstrated favourable ADMET profiles. MM-GB/SA analysis supported their high binding affinity toward PARP1. Molecular dynamics simulations revealed that compound B2 formed a stable complex within the PARP1 active site. In vitro assays showed enhanced cytotoxic activity of B2 against MCF-7 cells. DISCUSSION The findings highlight the effectiveness of combining computational and biological approaches to identify promising PARP1 inhibitors, with B2 showing strong binding, stability, and cytotoxicity, despite lacking in vivo validation. CONCLUSION Overall, compound B2 emerged as a promising PARP1 inhibitor with strong binding affinity, structural stability, and significant in vitro anticancer activity, warranting further optimization and preclinical investigation.