This is a pre-synopsis seminar. The zoom link to join this hybrid talk is as follows: https://zoom.us/j/94959990949 Meeting ID: 949 5999 0949 Passcode: 450678 Abstract: Human health is shaped by both genetics and lifetime environmental exposures. The exposome framework systematically captures such environmental exposures and links them to health outcomes. The chemical exposome is of special concern, as thousands of new chemicals are introduced into the global market annually, with many of them inevitably finding their way into the environment, yet their effects on human health remain poorly understood. Traditional toxicity testing relied heavily on slow and expensive animal studies, resulting in only a fraction of these chemicals being evaluated. In recent years, computational approaches have emerged as vital alternatives. They help organize toxicity data to prioritize chemicals for testing and provide mechanistic insights into their adverse effects. Therefore, in this thesis, we aim to link the chemical exposome to human health by systematically characterizing the chemical exposome and gaining mechanistic insights into chemical-induced adverse effects through diverse computational approaches. As the first objective, we focus on characterizing the chemical exposome associated with specific human health outcomes. We investigate the chemical exposome of vitiligo by curating a dedicated knowledgebase of its environmental chemical triggers. We next explore the chemical space to assess regulatory coverage, and utilize skin-specific transcriptomic signatures to gain mechanistic insights into the otherwise less understood etiology of this disease. Building on this, we extend our focus to endocrine disrupting chemicals (EDCs), a broader class of environmental chemicals associated with a wide range of adverse health effects. We update the Database of Endocrine Disrupting Chemicals and their Toxicity profiles (DEDuCT) by integrating data from diverse toxicological resources, providing a single window access to all EDC-relevant information. We further map these chemicals to Adverse Outcome Pathways (AOPs), and organize all compiled data into a large-scale toxicology knowledge graph, termed DEDuCT-KG, which we utilize to gain mechanistic insights into EDC-associated obesity and neurodegenerative disorders. As the second objective, we expand the scope of our computational investigation beyond human health, employing diverse network toxicology frameworks to assess the impacts of inorganic arsenic and cadmium on human and ecosystem health from a One Health perspective. We construct Aggregate Exposure Pathway (AEP) networks for the first time in the Indian context, and integrate them with stressor-AOP networks to trace complete source-to-outcome pathways. We compute Species Sensitivity Distributions (SSDs) from aquatic toxicity data to estimate hazard concentrations, and integrate them into stressor-species networks to identify particularly vulnerable species and aid in ecological risk assessment. Finally, we calculate Risk Quotients (RQs) to gauge the ecological risks associated with arsenic and cadmium in Indian rivers. Overall, the resources developed and frameworks utilized in this thesis provide results and insights that are easily accessible to the scientific community, laying the groundwork for future efforts towards a more complete characterization of the chemical exposome. The structured network toxicology investigation provides a basis for exploring the links between environmental chemical stressors and human and ecosystem health from a One Health perspective.