Few-shot meta-learning applied to whole brain activity maps improves systems neuropharmacology and drug discovery

Xuan Luo, Yanyun Ding, Yi Cao, Zhen Liu, Wenchong Zhang, Shangzhi Zeng, Shuk Han Cheng, Honglin Li, Stephen J. Haggarty, Xin Wang, Jin Zhang*, Peng Shi*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

1 Citation (Scopus)
24 Downloads (CityUHK Scholars)

Abstract

In this study, we present an approach to neuropharmacological research by integrating few-shot meta- learning algorithms with brain activity mapping (BAMing) to enhance the discovery of central nervous system (CNS) therapeutics. By utilizing patterns from previously validated CNS drugs, our approach facilitates the rapid identification and prediction of potential drug candidates from limited datasets, thereby accelerating the drug discovery process. The application of few-shot meta-learning algorithms allows us to adeptly navigate the challenges of limited sample sizes prevalent in neuropharmacology. The study reveals that our meta-learning-based convolutional neural network (Meta-CNN) models demonstrate enhanced stability and improved prediction accuracy over traditional machine-learning methods. Moreover, our BAM library proves instrumental in classifying CNS drugs and aiding in pharmaceutical repurposing and repositioning. Overall, this research not only demonstrates the effectiveness in overcoming data limitations but also highlights the significant potential of combining BAM with advanced meta-learning techniques in CNS drug discovery. © 2024 The Author(s). Published by Elsevier Inc.
Original languageEnglish
Article number110875
JournaliScience
Volume27
Issue number10
Online published3 Sept 2024
DOIs
Publication statusPublished - 18 Oct 2024

Funding

This work was supported by National Natural Science Foundation of China (U20A20194, 2222106, 12326605, 62331014), by General Research Fund (11215920, 11220024, 11218522, 11218523) from the Research Grants Council of Hong Kong SAR, Guangdong Basic and Applied Basic Research Foundation (2022B1515020082), Shenzhen-Hong Kong-Macau Science and Technology Program (Category C, SGDX2020110309300502), and Shenzhen Science and Technology Program (RCYX20200714114700072). Support from Innovation and Technology Commission of Hong Kong through the Centre for Cerebro-Cardiovascular Health Engineering and funds from City University of Hong Kong (7005084, 7005206, 7005642, 7020003, 7020077, 9680233, 9240060) are also acknowledged.

Publisher's Copyright Statement

  • This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

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