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

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. [Display omitted] •Integrating few-shot meta-learning with brain activity mapping for drug discovery•Utilization of high-throughput whole-brain activity mapping for pharmaceutical repurposing•Limited brain physiology data for fast and effective learning of pharmacological features•Successful identification of drug candidates by an advanced “learning to learn” approach Biological sciences; Natural sciences; Neuroscience; Pharmacology; Systems neuroscience