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Updated: Oct 9, 2025
A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
Published on: July 18, 2013
Machine learning prediction of antiviral-HPV protein interactions for anti-HPV pharmacotherapy
Hui-Heng Lin1, Qian-Ru Zhang2, Xiangjun Kong3
1Yuebei People's Hospital, Shantou University Medical College, No. 133 of Huimin South road, Wujiang District, Shaoguan City, 512025, China. molgen.v@gmail.com.
Abstract:
Persistent infection with high-risk types Human Papillomavirus could cause diseases including cervical cancers and oropharyngeal cancers. Nonetheless, so far there is no effective pharmacotherapy for treating the infection from high-risk HPV types, and hence it remains to be a severe threat to the health of female. Based on drug repositioning strategy, we trained and benchmarked multiple machine learning models so as to predict potential effective antiviral drugs for HPV infection in this work. Through optimizing models, measuring models' predictive performance using 182 pairs of antiviral-target interaction dataset which were all approved by the United States Food and Drug Administration, and benchmarking different models' predictive performance, we identified the optimized Support Vector Machine and K-Nearest Neighbor classifier with high precision score were the best two predictors (0.80 and 0.85 respectively) amongst classifiers of Support Vector Machine, Random forest, Adaboost, Naïve Bayes, K-Nearest Neighbors, and Logistic regression classifier. We applied these two predictors together and successfully predicted 57 pairs of antiviral-HPV protein interactions from 864 pairs of antiviral-HPV protein associations. Our work provided good drug candidates for anti-HPV drug discovery. So far as we know, we are the first one to conduct such HPV-oriented computational drug repositioning study.
Insights
This study used machine learning to identify potential drugs for high-risk Human Papillomavirus (HPV) infections. The research successfully predicted 57 antiviral-HPV protein interactions, offering new candidates for HPV drug discovery.
Area of Science:
- Computational biology
- Drug discovery
- Virology
Background:
- Persistent high-risk Human Papillomavirus (HPV) infection is a significant cause of cervical and oropharyngeal cancers.
- Currently, no effective pharmacotherapy exists for treating high-risk HPV infections, posing a severe health threat, particularly to women.
Purpose of the Study:
- To leverage drug repositioning and machine learning to predict effective antiviral drugs for high-risk HPV infection.
- To identify novel drug candidates for the development of anti-HPV therapies.
Main Methods:
- Trained and benchmarked multiple machine learning models (Support Vector Machine, K-Nearest Neighbor, Random Forest, Adaboost, Naïve Bayes, Logistic Regression) using a dataset of 182 FDA-approved antiviral-target interactions.
- Optimized and validated model performance, identifying Support Vector Machine and K-Nearest Neighbor as top predictors (precision scores of 0.80 and 0.85, respectively).
- Applied the best-performing models to predict antiviral-HPV protein interactions from a dataset of 864 associations.
Main Results:
- Identified optimized Support Vector Machine and K-Nearest Neighbor classifiers as the best predictors for antiviral drug efficacy against HPV.
- Successfully predicted 57 pairs of antiviral-HPV protein interactions from 864 potential associations.
- This computational approach represents the first study focused on drug repositioning for HPV.
Conclusions:
- The study provides promising drug candidates for anti-HPV drug discovery through a computational repositioning strategy.
- Machine learning models, particularly SVM and KNN, demonstrate high predictive performance in identifying potential antiviral agents for HPV.
- This work offers a novel computational approach to accelerate the development of treatments for high-risk HPV infections.
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