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Tamilanban Thamaraikani

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Open access Aug 2026

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.

Kawthar Alhussieni, R. Othman, Tamilanban Thamaraikani et al. · 1 citation
Aug 2026

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.

Aswin Krishnamurthy, Srikanth Jeyabalan, Yukthasree Prasanth Athikari et al. · 0 citations