Diseases emerge from perturbations in the cellular circuitry that extend beyond purely genetic aberrations. Proteins serve as critical effectors in these processes, driving and propagating signaling across biological networks. Proteins rarely act in isolation but instead work in intricate cellular networks. These networks, known as protein–protein interactions (PPI), are central to regulating cellular functions, and their dysregulation, can contribute significantly to the development of disease. To gain deeper insights, aggregate knowledge, and build novel understanding for drug targeting, sophisticated computational approaches and high-dimensional data are needed. Here we examine the latest artificial intelligence (AI)Artificial intelligence algorithms built on deep neural networkNeural network architectures (collectively called deep learningDeep learning) to dissect protein–protein interactions, their perturbations and utilizing this understanding for novel target discovery and drug design. We start with a brief overview of machine learningMachine learning approaches for PPI prediction before the rise of deep learning techniques. We then outline deep learning techniques developed to predict PPIs including cutting edge atomic resolution advancements introduced by AlphaFoldAlphaFold and deep learningDeep learning-guided rational design of PPI modulators. Finally, we explore the future of enhanced human-AI collaborations, highlighting our recent Hypothesis-Driven AI (HD-AI)Hypothesis-Driven AI approach which uses AI as a hypothesis-exploration and validation platform. Overall, this book chapter aims to propel the next wave of advancements in proteome-wide PPI studies and its applications in drug discovery.