In silico pipeline for GSK 3β inhibitor discovery in Alzheimer’s disease using pharmacophore screening, docking, ADME filtering, and MD validation

Integrated Virtual Screening and Molecular Electrostatic Surface Potential Descriptors for Predicting BACE1 Inhibitor Interactions in Alzheimer’s Disease

Developing brain-penetrant BACE1 inhibitors remains a major challenge in Alzheimer’s disease. Unlike conventional studies focusing solely on binding affinity, this work presents an integrated computational pipeline that prioritizes both potency and pharmacokinetic developability from the outset. A robust quantitative structure–activity relationship (QSAR) model was built for 41 N-(pyridin-3-yl)picolinamide derivatives using Molecular Electrostatic Surface Potential (MESP) descriptors. Principal component analysis identified three key electrostatic features – mean MESP, variance of negative MESP, and number of local MESP maxima/minima as drivers of BACE1 inhibitory activity. A key novelty is the early integration of Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) filtering, using ADMETlab 3.0, to ensure blood-brain barrier (BBB) permeability and low P-glycoprotein efflux properties whose absence contributed to the failure of several clinical BACE1 inhibitors. Only compound N-[3-[(1S,5S,6S)-3-amino-1-(difluoromethyl)-5-(fluoromethyl)-2-thia-4-azabicyclo[4.1.0]hept-3-en-5-yl]-4-fluorophenyl]-5-chloropyrazine-2-carboxamide (5) simultaneously satisfied the criteria for high predicted potency, favorable BBB penetration, low PgP efflux, and good synthetic accessibility. Molecular docking revealed that compound 5 binds strategically to the S1 and S3 substrate-recognition pockets through hydrogen bonds with THR280, GLN121, LYS155, as well as a halogen interaction with ARG283, while avoiding direct interaction with the catalytic dyad. This binding mode balances affinity and permeability, distinguishing compound 5 from clinically unsuccessful inhibitors. The practical implication is a rational framework that rapidly prioritizes candidates with high potency and favorable Central Nervous System (CNS) drug-like properties, thereby reducing the risk of late-stage attrition. All predictions are computational and require experimental validation (enzymatic, cellular, and in vivo) before any translational claims can be made. Thus, combining QSAR, ADMET, and molecular docking with an early emphasis on BBB penetration offers a time-efficient strategy for CNS drug discovery against Alzheimer’s disease.

A multistage computational pipeline integrating ligand-based and structure-based screening with ADMET evaluation, molecular dynamic simulation and free energy calculations for the discovery of potential TNIK inhibitors.

This LBVS-SBVS-ADMET-MD pipeline effectively identified three promising TNIK inhibitors, providing a solid foundation for future experimental validation and potential development of targeted therapies for Wnt-driven malignancies.

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

An Integrated Computational Workflow Combining NeuroScore, Network Pharmacology, and Molecular Dynamics Identifies a Benzothiadiazole‐Based SERT Inhibitor for Alzheimer's Disease

Alzheimer's disease (AD) lacks effective disease‐modifying therapies, highlighting the need for novel CNS‐targeted drug candidates. This study employs an integrated computational framework, combining a custom NeuroScore‐based CNS screening, network pharmacology, molecular docking, and molecular dynamics (MD) simulations, to identify 2,1,3‐benzothiadiazole derivatives as potential AD therapeutics. Screening 52,931 PubChem compounds yielded a top candidate (ID: 143693958; NeuroScore: 25.86; MW: 313.4 Da, LogP: 3.12, TPSA: 41.8 Å 2 ). Network pharmacology identified 87 AD‐overlapping targets, pinpointing serotonin transporter (SERT) as the key therapeutic target via serotonergic pathway enrichment. Molecular docking demonstrated strong SERT binding affinity (MolDock score: −132.72 kcal/mol), surpassing reference paroxetine (−130.51 kcal/mol). A 200 ns MD simulation confirmed structural stability (RMSD: 0.22 ± 0.03 nm; R g : 2.38 nm), and MM‐PBSA analysis revealed favorable binding free energy (−18.39 kJ/mol), driven predominantly by van der Waals interactions (−184.38 kJ/mol). ADMET profiling predicted excellent intestinal absorption (94.34%) with no AMES toxicity. This study presents a benzothiadiazole derivative as a promising SERT‐modulating lead for AD, establishing a robust computational foundation for experimental validation.

In silico discovery of RIOK3 inhibitors against pancreatic ductal adenocarcinoma using homology modelling, molecular docking, molecular dynamics simulations, ADMET prediction, and MTT assay

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer strongly linked to RIO Kinase 3 (RIOK3), which promotes progression by stabilizing and phosphorylating Focal Adhesion Kinase (FAK). Advances in protein structure prediction, particularly AlphaFold2, have significantly enhanced our understanding of protein dynamics, aiding in the identification of potential inhibitors for targeted therapies. This study used structure-based virtual screening, molecular dynamics simulations, ADMET/toxicity prediction, and in vitro validation to identify potential inhibitors of RIOK3 for PDAC treatment. The 3D structure of RIOK3 was predicted using AlphaFold2 and docked with compounds listed in the ZINC database that were independently confirmed as FDA-approved drugs using AutoDock Vina. Pharmacokinetic and pharmacodynamic properties were assessed with SwissADME, and in vitro validation was performed using MTT assays to assess cell viability and growth inhibition. Four top-scoring compounds were identified, with binding energies between − 11.3 and − 10.4 kcal/mol. Venetoclax showed the most stable complex with RIOK3, followed by Conivaptan and Irinotecan. Drospirenone showed weaker binding. Molecular dynamics simulations and MM/GBSA analysis supported the stability of these complexes. SwissADME and ProTox-II confirmed that the compounds met drug-likeness criteria but exhibited distinct pharmacokinetic and toxicity profiles. In vitro MTT assays showed concentration-dependent growth inhibition in PANC-1 cells, with Venetoclax having the lowest IC₅₀ value. This study identifies RIOK3 as a promising therapeutic target for PDAC, with Venetoclax, Conivaptan, Drospirenone, and Irinotecan as repurposable candidates for further research. Further studies should include biochemical assays, expanded cytotoxicity profiling in multiple PDAC cell lines, and in vivo evaluations to validate RIOK3-targeted therapies for PDAC treatment.

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.