A Comparative Study of Metric-based Meta-Learning Methods for Improving Few-Shot Learning in Drug Discovery with Limited Data

Drug discovery is a complex and costly process, often requiring extensive testing and significant financial investment. However, the acquisition of sufficient data for effective machine learning models presents a significant challenge. In this study, it is to investigate the application of few-shot learning—a machine learning approach that can function with low data—to address this challenge. Specifically, to investigate various metric-based meta-learning techniques within the context of drug discovery. The proposed research analysis includes Prototypical, Matching, Siamese, and Relation Networks, two promising few-shot learning methods. Through rigorous experimentation and comparative analysis, to evaluate the efficacy of these approaches across diverse datasets, drawing insights into their suitability for drug discovery tasks with limited data. The resultant findings shed light on the potential of few-shot learning techniques to accelerate drug discovery processes and highlight the importance of selecting appropriate methodologies based on the nature of the available data.