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DRPMKB1.0: A Comprehensive Knowledge Base for an AI-Oriented Drug Repositioning Prediction Model.

Xin Zheng1, Cheng Bi1, Weichen Bo2

  • 1Department of Respiratory and Critical Care Medicine, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.

Journal of Chemical Information and Modeling
|December 31, 2025
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Summary
This summary is machine-generated.

We developed DRPMKB 1.0, a knowledge base integrating AI models for drug repositioning (DR). This platform enhances prediction accuracy by personalizing model recommendations and standardizing model selection for efficient drug discovery.

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Area of Science:

  • Computational biology
  • Artificial intelligence in drug discovery
  • Pharmacology

Background:

  • Drug repositioning (DR) accelerates drug development by finding new uses for existing drugs.
  • The proliferation of artificial intelligence (AI) models in DR necessitates effective integration and resource management.
  • Large language models (LLMs) offer broad applicability but benefit from personalized knowledge bases for improved accuracy.

Purpose of the Study:

  • To develop a comprehensive, AI-oriented knowledge base for drug repositioning prediction (DRPMKB 1.0).
  • To create a standardized framework for evaluating and integrating diverse AI models and datasets for DR.
  • To enhance the accuracy and efficiency of personalized drug repositioning through tailored model recommendations.

Main Methods:

  • Compiled data from PubMed up to March 2024, covering 45 categories, 193 models, and 693 data entries.
  • Developed DRPMKB 1.0 with display and interaction interfaces across data, model, application, and reference dimensions.
  • Established a dual-evaluation framework to assess inherent model quality and predictive evidence.

Main Results:

  • DRPMKB 1.0 provides a centralized data-sharing platform for DR.
  • The dual-evaluation framework standardizes model selection and appraisal.
  • Personalized recommendations based on user data significantly improve DR prediction accuracy.

Conclusions:

  • DRPMKB 1.0 offers a robust, integrated platform for AI-driven drug repositioning.
  • The knowledge base facilitates seamless integration of diverse data and models, supporting continuous AI enhancement.
  • This resource empowers researchers with tailored model recommendations, advancing personalized drug discovery efforts.