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

Drug repositioning (DR) reduces the risks and costs of
drug development
by identifying new uses for approved drugs. The rapid growth of artificial
intelligence (AI) has led to many computational models. However, without
effective integration, excess models can waste resources and obscure
valuable ones. While large language models (LLMs) are preferred for
their broad applicability, integrating them with a personalized knowledge
base improves task-specific accuracy. Thus, we developed the AI-oriented
drug repositioning prediction model knowledge base (DRPMKB 1.0). This
knowledge base compiles data from PubMed up to March 2024, covering
two interfaces (display and interaction) and four dimensions (data,
model, application, and reference). It includes 45 categories, 193
models, and 693 data entries, offering a comprehensive data sharing
platform for DR. DRPMKB 1.0 establishes a dual-evaluation framework
to standardize model selection, appraising both inherent model quality
and the evidentiary support for its predictions. DRPMKB 1.0 integrates
diverse data and models for personalized DR, offering tailored model
recommendations based on user data, improving prediction accuracy.
DRPMKB 1.0 also offers a foundation for developers to integrate models
and data sets seamlessly. The knowledge base supports AI enhancement
through a knowledge base for continuous model refinement.