Advanced Learning and Bioinformatics in Innovative Drug Discovery Towards Bridging Biology

Abstract

Modern bioinformatics and artificial intelligence technologies are coming together to transform the process of developing pharmaceuticals, marking the beginning of an entirely novel phase in pharmaceutical discovery at the nexus of computational research and biological sciences. This work examines the relationship between statistics and biological processes, emphasizing the promise of significant change by combining artificial intelligence (AI) with bioinformatics methods to improve drug research. Incorporating computational methods, mathematical models, and machine learning procedures in computational biology tools improves our ability to understand biological phenomena's intricacies. The chapter examines the incorporation of few-shot and zero-shot learning approaches in drug discovery, emphasizing their significant potential in overcoming the obstacles related to scarce data and expediting the finding of novel targets for treatment. The review introduces the principle of few-shot learning and its practical implementation in drug research.