This paper is in the following e-collection/theme issue:

Drug Discovery and Clinical Trials (10) Cancer Prognosis Models and Machine Learning (75) Machine Learning (2195) Drug discovery and development (11) Precision Medicine (180)

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/77890, first published .
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

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

Authors of this article:

Samuel Kakraba1 Author Orcid Image ;   Srinivas Ayyadevara2 Author Orcid Image ;   Aayire Yadem Clement3 Author Orcid Image ;   Kuukua Egyinba Abraham4 Author Orcid Image ;   Cesar M Compadre5 Author Orcid Image ;   Robert J Shmookler Reis5 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

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