Exploring anti-dengue activity with atomic-weighted vectors, class balancing and machine learning

Dengue is a major mosquito-borne viral disease with no effective antiviral treatment currently available. This work introduces a machine-learning framework to predict anti-dengue activity in small molecules using Atomic-Weighted Vector (AWV) descriptors and data-balancing techniques. Sixteen datasets, each containing 2118 molecules, were generated with MD-LOVIs (Molecular Descriptor from Local Vertex Invariants) and preprocessed with IMMAN (Information theory-based CheMoMetric ANalysis), with Shannon entropy applied for feature selection. To address class imbalance (imbalance ratio = 6.66), the ADASYN algorithm was employed. Thirty classifiers spanning six methodological families were evaluated under two validation schemes (tenfold cross-validation and percentage split) on both balanced and imbalanced datasets. Performance was assessed using accuracy (ACC). Nonparametric statistical tests (Friedman, Nemenyi, Wilcoxon) indicated that data balancing improved model robustness. Tree-based and function-based classifiers achieved the best predictive performance. Overall, the proposed workflow offers a reproducible, data-driven approach for virtual screening of anti-dengue compounds and is readily extensible to other antiviral drug discovery tasks.