A Comparative Evaluation Framework Integrating Machine Learning and Deep Learning Models with ADME-Based Pharmacokinetic Assessment for HIV-Related Compounds

Background/Objectives: Predicting the bioactivity of HIV-related compounds is essential for early-stage drug discovery. However, most existing machine learning (ML) studies emphasize predictive performance while overlooking the predicted pharmacokinetic and drug-likeness properties of prioritized compounds. This study presents a comparative framework integrating classical ML, deep learning, graph-based models, and complementary ADME-based pharmacokinetic assessment. Methods: Twelve predictive models were evaluated using stratified five-fold cross-validation on the MoleculeNet HIV dataset under a unified experimental protocol. Model performance was assessed using multiple classification metrics together with statistical analysis. The highest-ranked compounds from the independent test set were further characterized using predicted ADME and drug-likeness properties. A representative compound (GDL1), prioritized by the GDL model, was subsequently evaluated by molecular docking against HIV-1 protease, HIV-1 integrase, and HIV-1 reverse transcriptase. Results: The graph-based GDL model achieved the highest ROC–AUC (0.956±0.015), followed by GRU (0.930±0.017) and RF (0.927±0.023). Statistical analysis indicated overall differences among model performances (Friedman test, p<0.001). However, Holm-corrected pairwise comparisons did not demonstrate statistically significant differences between the highest-performing models. Comparative ADME analysis showed that high predictive performance did not necessarily correspond to favorable predicted pharmacokinetic properties. Molecular docking suggested potential predicted binding interactions of the prioritized GDL1 compound with all three HIV-1 targets, with the most favorable predicted binding affinity observed for HIV-1 reverse transcriptase. Conclusions: The proposed framework enables a comprehensive comparison of diverse molecular learning approaches by integrating predictive performance with complementary predicted ADME, drug-likeness, and molecular docking analyses.