Machine Learning for Sequence‐to‐Function Approaches

This chapter explores the evolving field of machine learning (ML) for protein sequence-to-function prediction. It highlights how emerging ML architectures, such as protein language models (pLMs), can leverage extensive sequence data to establish links between proteins and their functions. Predicting protein functionality involves multiple tasks, such as gene ontology classification, enzyme commission assignments, and biophysical property estimation. The chapter reviews state-of-the-art ML models and architectures used for these tasks. These models can extract complex sequence patterns and generalize across diverse protein classes. It also highlights the importance of benchmarks and datasets for evaluating these computational methods. Furthermore, the chapter discusses novel ML techniques like contrastive and meta-learning, which aim to address challenges such as limited experimental data, class imbalances, and model interpretability. Ultimately, these ML advances have the potential to accelerate discoveries in biotechnology, drug development, and understanding genetic mechanisms.