Accessibility settings

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

Drug Discovery and Clinical Trials (29) Machine Learning (3321) Precision Medicine (453) Cancer Prognosis Models and Machine Learning (142) Drug discovery and development (16)

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

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 (9) 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
  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
  7. Sellami B, Elbouamri L, Samadi A, Yamari I, Chtita S. Machine learning–based 2D-QSAR modeling and rational design of quinazolinone-derived Schiff bases as potential COX-2 inhibitors. Scientific African 2026;33:e03552 View
  8. Alexandrino D, Rodrigues F, Santos S, Rocha Neto J, de Oliveira L. Interpretable machine learning models for predicting acetylcholinesterase inhibition by ionic liquids. Journal of Molecular Graphics and Modelling 2026;149:109581 View
  9. Aly Saad Aly M, Maiyza A, Abuelmakarem H, Mohamed S, Fournier-Viger P, Khan M. xLSTM-based deep learning model for predicting anticancer Activity of pyrano[3,2-a]phenazine hybrid derivatives: Proof of concept. Computational Biology and Chemistry 2027;126:109431 View