Navigating RNA Design with Machine Learning

This chapter explores the complexities of RNA design, focusing on the formulation of search spaces and the development of objective functions. It highlights the challenges of establishing a nuanced reward structure for evaluating RNA sequences and the innovative integration of meta-learning to improve model efficiency. Through discussions of hyperparameter optimization and ablation studies, the chapter illustrates the advantages of machine learning approaches over traditional methods in RNA sequence design.