Knowledge-Based Artificial Intelligence System for Drug Prioritization

In silico drug prioritization may be
a promising
and time-saving strategy to identify potential drugs, standing as
a faster and more cost-effective approach than de novo approaches. In recent years, artificial intelligence has greatly
evolved the drug development process. Here, we present a novel computational
framework for drug prioritization, labyrinth, 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 labyrinth, which is freely available on GitHub under the GPL-v2 license (https://github.com/hanjunwei-lab/labyrinth).