MPNN-CWExplainer: An enhanced deep learning framework for HIV drug bioactivity prediction with class-weighted loss and explainability

Abstract

Aims

Human Immunodeficiency Virus (HIV) remains a critical global health concern due to its impact on the immune system and its progression to Acquired Immunodeficiency Syndrome (AIDS) if untreated. While antiretroviral therapy has advanced significantly, challenges such as drug resistance, adverse effects, and viral mutation necessitate the development of novel therapeutic strategies. This study aims to improve HIV bioactivity prediction and provide interpretable insights into molecular determinants influencing bioactivity.

Materials and methods

We propose MPNN-CWExplainer, a novel graph-based deep learning framework for molecular property prediction. The model integrates a Message Passing Neural Network (MPNN) with a class-weighted loss function to effectively address class imbalance in HIV datasets. Furthermore, GNNExplainer is incorporated to provide post-hoc interpretability by identifying key atom- and bond-level substructures contributing to model predictions. Model robustness is ensured through Bayesian hyperparameter optimization and multiple independent runs.

Key findings

MPNN-CWExplainer achieved state-of-the-art predictive performance on the HIV dataset, with an AUC-ROC of 87.631 % and AUC-PRC of 86.02 %, surpassing existing baseline models. The class-weighted approach enhanced minority class representation, and GNNExplainer successfully highlighted chemically meaningful substructures correlating with bioactivity.

Significance

The proposed framework not only improves prediction accuracy for HIV bioactivity but also enhances transparency and interpretability, crucial for medicinal chemists in understanding model behaviour. MPNN-CWExplainer serves as a robust and interpretable tool for computational drug discovery, supporting informed decision-making in lead optimization and molecular design.