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Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/77890, first published .
Scientist in lab coat and goggles examines DNA helix on futuristic touchscreen.

Machine Learning–Enhanced Quantitative Structure-Activity Relationship Modeling for DNA Polymerase Inhibitor Discovery: Algorithm Development and Validation

Machine Learning–Enhanced Quantitative Structure-Activity Relationship Modeling for DNA Polymerase Inhibitor Discovery: Algorithm Development and Validation

Journals

  1. Wenzheng H, Agyemang E, Srivastav S, Shaffer J, Kakraba S. Artificial Intelligence–Enhanced Multi-Algorithm R Shiny Application for Predictive Modeling and Analytics: Case Study of Alzheimer Disease Diagnostics. JMIR Aging 2025;8:e70272 View
  2. Kakraba S, Agyemang E, Shmookler Reis R. Accelerating Discovery of Leukemia Inhibitors Using AI-Driven Quantitative Structure-Activity Relationship: Algorithm Development and Validation. JMIR AI 2026;5:e81552 View
  3. Zhu Z, Hu G, Jia G. Artificial Intelligence in Adverse Outcome Pathways: A Review of Strategies for Automated Information Extraction, Quantitative Analysis, and Iterative Optimization. Occupational Health 2026;1(1):9 View
  4. Kakraba S, Agyemang E, Srivastav S. Cognitive sovereignty and decolonial public health: reclaiming epistemic authority in the global AI era. Frontiers in Public Health 2026;14 View
  5. Kakraba S, Yadem A, Abraham K, Chaudhry F, Agyemang E. Unraveling protein secrets: machine learning unveils novel biologically significant associations among amino acids. Network Modeling Analysis in Health Informatics and Bioinformatics 2026;15(1) View
  6. Candanedo D, Agyemang E, Chaudhry F, Franks T, Taylor B, Siliezar K, Kakraba S. Leveraging machine learning algorithms and explainable AI for predicting mental health disorder treatment at the workplace. Acta Psychologica 2026;267:107081 View