Comput Biol Med. 2025 Dec 7;200:111372. doi: 10.1016/j.compbiomed.2025.111372. Online ahead of print.

ABSTRACT

The emergence of monkeypox virus (MPXV) as a global health threat has necessitated the rapid identification of novel antiviral therapeutics. Currently, no FDA-approved drugs are specifically designed against the disease. We used an in-house deep learning pharmacophore model for screening a library of 1974 FDA-approved drugs targeting the active site of MPXV DNA polymerase. Three drugs exhibited the strongest binding affinities, outperforming the control drug, Cidofovir diphosphate, and forming stable interactions with key active site residues. Among them, Paromomycin emerged as the most favourable drug, demonstrating stable, persistent, and adaptable interactions in molecular dynamics simulation. In parallel, we developed a novel automated peptide-generating AI pipeline that integrates active-site residues with knowledge-guided amino acid selection to generate and evaluate synthetic peptides. Cysteine-Phenylalanine-Cysteine (CFC), together with a panel of candidates, emerged through rational balancing of physicochemical properties and drug-likeness for accelerated therapeutic discovery. Synthetic peptides were evaluated to further understand the binding efficacies with DNA polymerase. CFC peptide demonstrated strong binding affinity (-8.08 kcal/mol) through stable interactions with key catalytic residues ASP549, ARG634 and LYS661, while MMGBSA analysis confirmed favourable binding energy (-33.02 kcal/mol). Consistent results in MD simulations indicate functional binding without destabilisation. Although ADMET predictions for CFC revealed limitations in permeability and oral bioavailability, its favourable binding profile and reduced predicted toxicity support its potential as a novel antiviral lead.

PMID:41360016 | DOI:10.1016/j.compbiomed.2025.111372

World J Hepatol. 2025 Nov 27;17(11):109494. doi: 10.4254/wjh.v17.i11.109494.

ABSTRACT

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality globally, necessitating advanced diagnostic tools to improve early detection and personalized targeted therapy. This review synthesizes evidence on explainable ensemble learning approaches for HCC classification, emphasizing their integration with clinical workflows and multi-omics data. A systematic analysis [including datasets such as The Cancer Genome Atlas, Gene Expression Omnibus, and the Surveillance, Epidemiology, and End Results (SEER) datasets] revealed that explainable ensemble learning models achieve high diagnostic accuracy by combining clinical features, serum biomarkers such as alpha-fetoprotein, imaging features such as computed tomography and magnetic resonance imaging, and genomic data. For instance, SHapley Additive exPlanations (SHAP)-based random forests trained on NCBI GSE14520 microarray data (n = 445) achieved 96.53% accuracy, while stacking ensembles applied to the SEER program data (n = 1897) demonstrated an area under the receiver operating characteristic curve of 0.779 for mortality prediction. Despite promising results, challenges persist, including the computational costs of SHAP and local interpretable model-agnostic explanations analyses (e.g., TreeSHAP requiring distributed computing for metabolomics datasets) and dataset biases (e.g., SEER's Western population dominance limiting generalizability). Future research must address inter-cohort heterogeneity, standardize explainability metrics, and prioritize lightweight surrogate models for resource-limited settings. This review presents the potential of explainable ensemble learning frameworks to bridge the gap between predictive accuracy and clinical interpretability, though rigorous validation in independent, multi-center cohorts is critical for real-world deployment.

PMID:41358057 | PMC:PMC12679159 | DOI:10.4254/wjh.v17.i11.109494