Abstract: With the rapid accumulation of high-throughput data in biomedicine, it comes to be possible to break through the traditional drug design system and establish a rapid discovery method for antifungal drugs starting from rich data characteristics of biomedical information. High-throughput omics data are to calculate similar pharmacodynamic relationships between discovered drugs, which is applied into antifungal drugs discovery. Based on the CMAP and LINCS data platforms, we obtain the cell transcriptome data under the action of the compound as characteristic characterizations of cell's drug effects. Then we measure the similarity between the characterizations with GSEA method and WTCS algorithm. After that, we screen potential antifungal drugs by the comprehensive rank of the similarity of the drugs to be screened and known antifungal drugs. Based on the large-scale calculations of existing antifungal drugs, we found that drugs such as prenylamine and iri-notecan are expected to become antifungal drug candidates, and some of them are supported by related studies, which need to be verified by further experiments. This paper applies biological big data to quick drug rational design and provides important calculation methods for rational design of antifungal drug repositioning, and inspires and accelerates the development of existing antifungal drugs.

Key words: drug repositioning, biomedical big data, antifungal drug