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Identifying antivirals against influenza PA endonuclease with machine learning-based activity prediction, DFT optimization, and molecular dynamics simulation

Alharby TN, Alanazi M, Khan KU, Abouzied AS

Mol Divers, 2026 Apr;30(2):2511-2524.
PMID: 41264202 DOI: 10.1007/s11030-025-11403-3

Increasing concern about highly pathogenic avian influenza A (H5N1) is prompting the development of new antivirals directed toward conserved viral entities that are resistant to mutational escape. Here, at a multi-scale and precision-guided computational level, we employed a set of procedures to identify potential small-molecule inhibitors of the influenza virus PA endonuclease, a central component of the viral RNA polymerase complex responsible for cap-snatching of mRNA transcription. Through the structurally diverse drug-like dataset, we initiated structure-based virtual screens against the PA catalytic domain and received 1,500 high-affinity candidates. Top-scoring candidates were optimized using quantum mechanical density functional theory (DFT) computations and electron reactivity/orbital distribution analyses. Through re-docking of optimized geometries using DFT, lead molecules were subjected to exhaustive 1-microsecond molecular dynamics (MD) simulations and MM/GBSA binding free energy decomposition and principal component analysis (PCA) sampling of dynamic conformational topographies. Free energy surface mapping of low-energy basins and superimposition validation of pose stabilities verified sub-angstrom deviations. Significantly, 24782939 registered the least thermodynamic profile (ΔG = -45.8 kcal/mol), greatest H-bond persistence, and computed pIC50 of 8.17 using a machine-learned predictive model trained against structurally diverse chemical scaffolds. This multi-scale, integrated framework, involving atomic, energetic, and predictive scales, holds promise for translational applications of computational pipelines in antiviral discovery. Our findings nominate 24,782,939 as a highly promising inhibitor of PA endonuclease and have the potential to be developed into a next-gen therapeutic candidate against influenza A viruses.
Fulltext Targeting Breast Cancer Signaling via Phytomedicine and Nanomedicine

Ansari JA, Malik JA, Ahmed S, Bhat FA, Khanam A, Mir SA, et al.

Pharmacology, 2023;108(6):504-520.
PMID: 37748454 DOI: 10.1159/000531802

BACKGROUND: The development of breast cancer (BC) and how it responds to treatment have both been linked to the involvement of inflammation. Chronic inflammation is critical in carcinogenesis, leading to elevated DNA damage, impaired DNA repair machinery, cell growth, apoptosis, angiogenesis, and invasion. Studies have found several targets that selectively modulate inflammation in cancer, limit BC's growth, and boost treatment effectiveness. Drug resistance and the absence of efficient therapeutics for metastatic and triple-negative BC contribute to the poor outlook of BC patients.
SUMMARY: To treat BC, small-molecule inhibitors, phytomedicines, and nanoparticles are conjugated to attenuate BC signaling pathways. Due to their numerous target mechanisms and strong safety records, phytomedicines and nanomedicines have received much attention in studies examining their prospects as anti-BC agents by such unfulfilled demands.
KEY MESSAGES: The processes involved in the affiliation across the progression of tumors and the spread of inflammation are highlighted in this review. Furthermore, we included many drugs now undergoing clinical trials that target cancer-mediated inflammatory pathways, cutting-edge nanotechnology-derived delivery systems, and a variety of phytomedicines that presently address BC.

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