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

Journal: Life sciences
Published Date: Jul 3, 2025

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.

Authors

  • Aga Basit Iqbal
    Department of Computer Science and Engineering, Islamic University of Science and Technology, Awantipora, Jammu & Kashmir, India.
  • Assif Assad
    Department of Computer Science and Engineering, Islamic University of Science and Techonology Kashmir, Awantipora, 192122, J&K, India. Electronic address: assif.assad@islamicuniversity.edu.in.
  • Basharat Bhat
    Centre for Artificial Intelligence and Machine Learning, Sher-e-Kashmir University of Agricultural Sciences and Technology, Shalimar, Srinagar, Jammu & Kashmir, India.
  • Muzafar A Macha
    Watson-Crick Centre for Molecular Medicine, Islamic University of Science and Technology, Awantipora, Jammu & Kashmir, India.
  • Syed Zubair Ahmad Shah
    Department of Computer Science and Engineering, Islamic University of Science and Technology, Awantipora, Jammu & Kashmir, India. Electronic address: zubair.shah@islamicuniversity.edu.in.

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