Computational and Structure-Guided E-Pharmacophore-Based Virtual Screening for the Identification of Novel NEK2 Kinase Inhibitors as Potential Anticancer Agents

Structure-Based Virtual Screening and De Novo Design Guided by 3D-QSAR Triage for the Identification of Nanomolar STK3 Inhibitors

Serine/threonine kinase 3 (STK3) is a core kinase in the Hippo signaling pathway that regulates cell proliferation, differentiation, and apoptosis. Dysregulation of this pathway is linked to various cancers and immune disorders, emphasizing STK3 as a promising therapeutic target for diseases that involve abnormal cell growth. Despite their therapeutic relevance, only a limited number of potent STK3 inhibitors have been reported, likely due to the difficulty in designing molecules that can bind tightly to the ATP-binding site of STK3. Using the 2-(2-amino-5-phenylpyrimidin-4-yl)-5-methoxyphenol (APPP) scaffold identified through virtual screening, we discovered a series of novel, potent STK3 inhibitors with half-maximal inhibitory concentration (IC50) values as low as 3.52 nM. This was made possible through an integrated molecular design strategy that combined structure-based de novo design with a predictive 3D-QSAR model to estimate IC50 values and prioritize compounds for synthesis. This approach improved the prioritization of compounds for synthesis by enhancing binding energy discrimination beyond the limitations of scoring functions in de novo design. Strategic derivatization at four defined positions on the APPP core enhanced interactions in the ATP-binding site of STK3, resulting in 18 of the 25 synthesized derivatives exhibiting IC50 values below 100 nM. These findings are particularly significant given the scarcity of known STK3 inhibitors with comparable potency, while also indicating that 3D-QSAR modeling contributed as a complementary triage tool rather than a standalone driver of lead discovery. This advancement stems from the use of quantum-mechanically derived 3D structural alignments and the incorporation of electrostatic potential distributions as quantitative molecular descriptors. Taken together, this study establishes a robust design paradigm for the rapid development of highly potent kinase inhibitors through structure-based computational strategies.

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

The findings from molecular docking, 500-ns molecular dynamics simulations, MM-GBSA calculations, and alanine scanning analyses collectively corroborate a stable binding mode of BTB11556 within the MPO active site, support further investigation of BTB11556 as a candidate compound associated with MPO-targeted therapeutic strategies.

In Silico Exploration of Novel EGFR T790M and C797S Resistance with Mutant-Selective Allosteric Inhibitors Using a Hybrid Computational Approach

A number of lead candidates with strong EGFR inhibitory potential, promising pharmacokinetic profiles, and mutant selectivity were successfully identified by the integrated computational approach.

From virtual screening to cell-based assays: structure-guided identification of EGFR-targeting perimidine derivatives with anticancer activity.

Structure-based elucidation of thiazine inhibitors targeting estrogen receptors alpha: pharmacophore modeling, virtual screening, molecular docking, MD, and DFT approach.

This research developed a promising lead molecule (HIT1) as a therapeutic approach for ER-positive breast cancer through pharmacophore model-based drug design of thiazine derivative targeting ERα via pharmacophore model-based drug design.

Identification of the Potential of Plant-Derived Agents to Act as KRAS G12D Inhibitors Through Molecular Docking, Molecular Dynamics Simulations, and MMGBSA.

INTRODUCTION/OBJECTIVE The KRAS G12D mutation is a significant oncogenic factor that promotes cell proliferation and tumor development, particularly in pancreatic cancer. Inhibiting the mutated form of the KRAS protein is a critical strategy in cancer treatment. While molecules like MRTX1133 show promise, the limited efficacy and safety of current treatment options highlight the need to discover new and safer therapeutic candidates. METHODS Approximately 1000 plant-derived bioactive compounds downloaded from the literature and Dr. Duke's database were filtered according to Lipinski's Rules and pharmacokinetic properties to create a set of 425 candidate molecules. Geometry optimization and QSAR parameters were calculated using Spartan software, and molecular docking and binding affinities were calculated using AutoDock Vina. MRTX1133 was used as a reference in the evaluations. Protein-ligand interaction maps were generated, and key interaction residues were identified using BIOVIA Discovery Studio. Molecular dynamics simulations of 200 ns were performed using Schrödinger-Desmond for the six compounds with the best binding affinities, and MMGBSA binding free energy calculations were performed. RESULTS The results showed that Isochlorogenic acid, Coniferin, Neochlorogenic acid, Cryptochlorogenic acid, Gamma-L-glutamyl-L-phenylalanine, and Chlorogenic acid demonstrated good bonding affinity to KRAS G12D with -11.0, -9.8, -9.5, -9.1, -9, and -8.9 Kcal.mol-1, respectively, compared to MRTX1133 (-8.3 Kcal.mol-1), exhibiting favorable physicochemical profiles and revealing their potential as KRAS G12D inhibitors. Based on bonding scores and QSAR data, four of the six selected molecules (Coniferin, Gamma-Lglutamyl- L-phenylalanine, Isochlorogenic acid, and Neochlorogenic acid) formed stable protein-ligand complexes with mean RMSD values < 2.5 Å during 200 ns MD simulations. MMGBSA analysis confirmed the binding free energies, and the compound giving the strongest interaction was determined to be Coniferin with ΔG_bind = -72.53 ± 5.24 Kcal.mol-1. Superior docking scores and stable MD trajectories demonstrate that the selected plant-derived compounds, especially Coniferin, effectively interact with the KRAS G12D binding pocket via suitable hydrophobic and hydrogen-bond contacts. These findings suggest that natural compounds can provide structurally diverse and potentially safer alternatives compared to synthetic inhibitors; however, experimental validation is necessary to confirm their inhibitory potential and selectivity. DISCUSSION According to the MMGBSA results, these compounds have suitable binding free energy values comparable to the reference, with Coniferin as the strongest plant-derived candidate with ΔGbind = -72.53 ± 5.24 Kcal/mol-. CONCLUSION As a result of this in silico study, coniferin, neochlorogenic acid, and chlorogenic acid have been identified as potential precursor molecules for KRAS G12D inhibition. Isochlorogenic acid was excluded due to its dynamic instability. These plant-derived compounds require further experimental validation as potential anticancer agents targeting KRAS G12D mutations.