Functional microRNA-targeting drug discovery by graph-based deep learning

Arash Keshavarzi Arshadi; Milad Salem; Heather Karner; Kristle Garcia; Abolfazl Arab; Jiann Shiun Yuan; Hani Goodarzi

doi:10.1016/j.patter.2023.100909

Functional microRNA-targeting drug discovery by graph-based deep learning

Patterns (N Y). 2024 Jan 3;5(1):100909. doi: 10.1016/j.patter.2023.100909. eCollection 2024 Jan 12.

Authors

Arash Keshavarzi Arshadi^{1

2

3

4}, Milad Salem⁵, Heather Karner^{1

2

3

4}, Kristle Garcia^{1

2

3

4}, Abolfazl Arab^{1

2

3

4}, Jiann Shiun Yuan⁵, Hani Goodarzi^{1

2

3

4}

Affiliations

¹ Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA.
² Department of Urology, University of California, San Francisco, San Francisco, CA, USA.
³ Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
⁴ Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
⁵ Department of Computer Engineering, University of Central Florida, Orlando, FL, USA.

Abstract

MicroRNAs are recognized as key drivers in many cancers but targeting them with small molecules remains a challenge. We present RiboStrike, a deep-learning framework that identifies small molecules against specific microRNAs. To demonstrate its capabilities, we applied it to microRNA-21 (miR-21), a known driver of breast cancer. To ensure selectivity toward miR-21, we performed counter-screens against miR-122 and DICER. Auxiliary models were used to evaluate toxicity and rank the candidates. Learning from various datasets, we screened a pool of nine million molecules and identified eight, three of which showed anti-miR-21 activity in both reporter assays and RNA sequencing experiments. Target selectivity of these compounds was assessed using microRNA profiling and RNA sequencing analysis. The top candidate was tested in a xenograft mouse model of breast cancer metastasis, demonstrating a significant reduction in lung metastases. These results demonstrate RiboStrike's ability to nominate compounds that target the activity of miRNAs in cancer.

Keywords: RNA-targeting drug discovery; artificial intelligence; deep learning; drug toxicity evaluation; graph convolutional neural network; in silico drug screening; microRNA inhibition; microRNA-21.

Abstract

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