A Comparative Evaluation Framework Integrating Machine Learning and Deep Learning Models with ADME-Based Pharmacokinetic Assessment for HIV-Related Compounds
Aug 2026· Pharmaceuticals· Vol 19, pp. 1267· 0 citations· 58 references
Medicine
TL;DR
A comparative framework integrating classical ML, deep learning, graph-based models, and complementary ADME-based pharmacokinetic and drug-likeness assessment is presented to enable a comprehensive comparison of diverse molecular learning approaches.
Abstract
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.
Accurate prediction of compound bioactivity is essential for accelerating antiviral drug discovery and reducing experimental costs. Machine learning (ML) methods have shown considerable promise in modeling structure–activity relationships and compound potency. In this study, we present an integrated ML framework for predicting IC50 and pIC50 values of compounds active against SARS-CoV-2, key indicators of antiviral potency. The proposed framework comprises three complementary approaches: (i) a regression model for quantitative IC50 prediction validated against experimental data; (ii) a classification model that categorizes compounds into active and inactive classes to support compound prioritization; and (iii) a multi-task neural network that jointly performs IC50 regression and activity classification, enhancing predictive performance and interpretability. A distinctive feature of this work is the incorporation of ligand efficiency (LE) as a criterion for activity classification, offering an alternative perspective on compound prioritization that has not been previously explored in SARS-CoV-2 bioactivity modeling. The proposed models demonstrate strong predictive capability, achieving a coefficient of determination (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2$$\end{document}) of 0.77 using a neural network with feature selection, while the Random Forest classifier attains an accuracy, precision, and recall of approximately 0.92. These results highlight the potential of integrated regression, classification, and multi-task learning approaches as scalable and cost-effective tools for SARS-CoV-2 bioactivity prediction and antiviral drug discovery.
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