scDrug+: predicting drug-responses using single-cell transcriptomics and molecular structure

Yih-Yun Sun; Chiao-Yu Hsieh; Jian-Hung Wen; Tzu-Yang Tseng; Jia-Hsin Huang; Yen-Jen Oyang; Hsuan-Cheng Huang; Hsueh-Fen Juan

doi:10.1016/j.biopha.2024.117070

scDrug+: predicting drug-responses using single-cell transcriptomics and molecular structure

Biomed Pharmacother. 2024 Aug:177:117070. doi: 10.1016/j.biopha.2024.117070. Epub 2024 Jul 3.

Authors

Yih-Yun Sun¹, Chiao-Yu Hsieh², Jian-Hung Wen³, Tzu-Yang Tseng⁴, Jia-Hsin Huang², Yen-Jen Oyang⁵, Hsuan-Cheng Huang⁶, Hsueh-Fen Juan⁷

Affiliations

¹ Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taiwan; Taiwan AI Labs, Taipei 10351, Taiwan.
² Taiwan AI Labs, Taipei 10351, Taiwan.
³ Taiwan AI Labs, Taipei 10351, Taiwan; Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan.
⁴ Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taiwan; Department of Life Science, National Taiwan University, Taipei 106, Taiwan.
⁵ Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taiwan.
⁶ Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan. Electronic address: [email protected].
⁷ Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taiwan; Taiwan AI Labs, Taipei 10351, Taiwan; Department of Life Science, National Taiwan University, Taipei 106, Taiwan; Center for Computational and Systems Biology, National Taiwan University, Taipei 106, Taiwan; Center for Advanced Computing and Imaging in Biomedicine, National Taiwan University, Taipei 106, Taiwan. Electronic address: [email protected].

PMID: 38964180
DOI: 10.1016/j.biopha.2024.117070

Abstract

Predicting drug responses based on individual transcriptomic profiles holds promise for refining prognosis and advancing precision medicine. Although many studies have endeavored to predict the responses of known drugs to novel transcriptomic profiles, research into predicting responses for newly discovered drugs remains sparse. In this study, we introduce scDrug+, a comprehensive pipeline that seamlessly integrates single-cell analysis with drug-response prediction. Importantly, scDrug+ is equipped to predict the response of new drugs by analyzing their molecular structures. The open-source tool is available as a Docker container, ensuring ease of deployment and reproducibility. It can be accessed at https://github.com/ailabstw/scDrugplus.

Keywords: Drug-responses; Machine learning; Precision medicine; Single-cell transcriptomics.

MeSH terms

Drug Discovery / methods
Gene Expression Profiling* / methods
Humans
Molecular Structure
Reproducibility of Results
Single-Cell Analysis* / methods
Software
Transcriptome* / genetics