Learnable protein representations in computational biology for predicting drug-target affinity.

In this review, we discuss the various different types of learnable protein representations that have been used in computational biology, with a particular focus on representations that have been used in the paradigm of predicting drug-target affinity. We explore this from multiple perspectives: the source of protein information used, the training paradigms used in generating and applying such representations, and the types of (deep-learning-based) encoding or embedding methods that have been used to generate and operate on such representations. We focus on drug-target affinity due to its particular relevance and utility in the field of drug development and assessment, and we make suggestions for how drug-target affinity prediction methods development can be further improved by examining the current literature from the aforementioned perspectives. This survey thus serves as a valuable resource for researchers seeking to develop methods for predicting drug-target affinity by exploring how protein information has been used and could be used in effective ways to improve such predictions.