Deep learning enhanced deciphering of brain activity maps for discovery of therapeutics for brain disorders
Xianrui Zhang,
Zhen Liu,
Xuan Luo,
Yi Cao,
Wencong Zhang,
Honglin Li,
Wei Li,
Shuk Han Cheng,
Stephen J. Haggarty,
Xin Wang,
Peng Shi
Affiliations
Xianrui Zhang
Department of Biomedical Science, City University of Hong Kong, Kowloon, Hong Kong SAR 999077, China
Zhen Liu
Department of Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR 999077, China
Xuan Luo
Department of Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR 999077, China
Yi Cao
Department of Surgery, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR 999077, China; Department of Biomedical Science, City University of Hong Kong, Kowloon, Hong Kong SAR 999077, China
Wencong Zhang
Department of Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR 999077, China
Honglin Li
Innovation Center for AI and Drug Discovery, East China Normal University, Shanghai 200062, China
Wei Li
School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, China
Shuk Han Cheng
Department of Biomedical Science, City University of Hong Kong, Kowloon, Hong Kong SAR 999077, China
Stephen J. Haggarty
Chemical Neurobiology Laboratory, Precision Therapeutics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA 02114, USA
Xin Wang
Department of Surgery, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR 999077, China; Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, Guangdong 518057, China; Corresponding author
Peng Shi
Department of Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR 999077, China; Hong Kong Centre for Cerebro-Cardiovascular Health Engineering, Hong Kong Science Park, Hong Kong SAR 999077, China; Shenzhen Research Institute, City University of Hong Kong, Shenzhen, Guangdong 518057, China; Corresponding author
Summary: This study presents an artificial intelligence enhanced in vivo screening platform, DeepBAM, which enables deep learning of large-scale whole brain activity maps (BAMs) from living, drug-responsive larval zebrafish for neuropharmacological prediction. Automated microfluidics and high-speed microscopy are utilized to achieve high-throughput in vivo phenotypic screening for generating the BAM library. Deep learning is applied to deconvolve the pharmacological information from the BAM library and to predict the therapeutical potential of non-clinical compounds without any prior information about the chemicals. For a validation set composed of blinded clinical neuro-drugs, several potent anti-Parkinson’s disease and anti-epileptic drugs are predicted with nearly 45% accuracy. The prediction capability of DeepBAM is further tested with a set of nonclinical compounds, revealing the pharmaceutical potential in 80% of the anti-epileptic and 36% of the anti-Parkinson predictions. These data support the notion of systems-level phenotyping in combination with machine learning to aid therapeutics discovery for brain disorders.
