Knowledge-BasedArtificial Intelligence System forDrug Prioritization

<i>In silico</i> drug prioritization may be a promising and time-saving strategy to identify potential drugs, standing as a faster and more cost-effective approach than <i>de novo</i> approaches. In recent years, artificial intelligence has greatly evolved the drug development process. Here, we present a novel computational framework for drug prioritization, <i>labyrinth</i>, designed to simulate human knowledge retrieval and inference to identify potential drug candidates for each disease. With the integration of up-to-date clinical trials, literature co-occurrences, drug–target interactions, and disease similarities, our framework achieves over 90% predictive accuracy across clinical trial phases and strong alignment with clinical practice in TCGA cohorts. We have demonstrated effectiveness across 20 different disease categories with robust ROC-AUC metrics and the balance between predictive accuracy and model interpretability. We further demonstrate its effectiveness at both the population and the individual levels. This study not only demonstrates the capacity for its drug prioritization but underscores the importance of aligning computational models with intuitive human reasoning. We have wrapped the core function into an R package named <i>labyrinth</i>, which is freely available on GitHub under the GPL-v2 license (https://github.com/hanjunwei-lab/labyrinth).