The human proteome comprises proteins that rarely act alone, often carrying out their functions through direct interactions with other proteins or as part of larger complexes. Perturbations in these proteins can destabilize their structure, hindering their ability to form these protein-protein interactions (PPIs). These disrupted PPIs impact biological processes and can contribute to the development of various diseases, posing a significant challenge in treating conditions with complex, multifactorial pathophysiology, such as Alzheimer’s disease (AD) and cancer. Thus, achieving a comprehensive understanding of the molecular mechanisms driving these complex conditions becomes crucial for developing more targeted and effective therapeutics. This dissertation utilizes computational tools to analyze protein interaction networks, identify disease-related perturbation and deepen our understanding of therapeutic mechanisms, with the ultimate aim of advancing the development of more targeted treatments. Chapter 1 introduces relevant background to set the context for the work presented herein. Chapter 2 provides a comprehensive review of the advancements in both the experimental and computational aspects of cross-linking mass spectrometry (XL-MS) workflow. XL-MS is a powerful technique for elucidating PPIs and their structural context that, when integrated with multi-omics data, can be utilized by machine learning algorithms to predict how disruptions in protein interactions affect cellular networks and drive disease progression. Chapter 3 describes my contributions to development of the PIONEER web server tool and on-demand prediction pipeline, associated with the deep learning framework, which uses available structural information to make predictions about PPI interfaces. Such web server tools offer a system-level perspective on perturbation in PPI interfaces and their downstream effects. By offering a user-friendly web-based tool with predictive capabilities that integrates various levels of protein structure information with disease-associated mutations, PIONEER represents a notable step forward in building computational tools accessible to the wider scientific community to advance precision medicine. In Chapter 4, computational approaches are applied to predict mechanisms of pCDP-DB, a novel cis-locked cyclic dipeptide that alters the fate of the amyloid precursor protein (APP) and its cleavage products, thereby reducing amyloidogenic processing of APP and/or inducing clearance of sAPP_ and A_ from H4 neuroglioma cells. Previously obtained pCDP-DB interactomes were analyzed by generating PPI networks, applying a clustering algorithm to elucidate highly interconnected subgroups of proteins, then identifying statistically enriched pathways within these communities. Our findings reveal pleiotropic effects of pCDP-DB on several pathways dysregulated in AD. These include phago-lysosomal pathways and proteasome/autophagy systems that could contribute to enhanced A_ clearance. Integration of these findings with existing literature contextualizes the role of enriched pathways. This combined approach of data-driven discovery and literature-based validation generates predictions about pCDP-DB’s mechanisms of action, guiding further experimental studies for mechanistic validation. By systematically analyzing PPI networks, this work establishes a framework for uncovering protein communities, identifying dysregulated pathways, and developing more targeted treatments for human diseases. Overall, this research underscores the transformative potential of computational tools in precision medicine, bridging the gap between molecular understanding of complex diseases and the development of more precise therapeutic strategies.