The identification of novel protein drug targets is a cornerstone of drug discovery, directly influencing the development of new and more effective therapies. With the growing availability of genomic and proteomic data, computational methods based on protein sequence analysis have become increasingly integral to target the identification. This chapter explores in detail methods and strategies for discovering protein drug targets based on sequence data. We begin by discussing function prediction techniques that rely on protein sequence similarity, allowing researchers to infer the biological roles of proteins and their relevance to disease mechanisms. The chapter then reviews reliable sources of drug target information, such as curated databases and experimental datasets, which serve as foundational resources in target discovery. A key focus is placed on the concept of druggability—how sequence-based comparisons can help identify proteins that are more likely to bind with therapeutic molecules. Finally, we investigate the significance of sequence-derived properties, such as conserved motifs and structural domains, in determining the druggability of targets. Throughout, we highlight both the advantages and limitations of these sequence-based approaches, providing insights into the challenges of translating computational predictions into clinically viable drug targets. By synthesizing the latest advancements, this chapter offers a comprehensive understanding of how protein sequence data can be leveraged to discover novel drug targets, with implications for the future of precision medicine.