In response to PHS 2020-1,Topic 019 entitled, “Data Science Tools for Accelerating Alcohol Research”, Netrias and the Waggoner Center for Alcohol and Addiction Research (WCAAR) propose to develop ASSIST, a data science platform that will use deep learning to aid alcohol researchers in the discovery of drug targets clinically linked to Alcohol Use Disorder (AUD). ASSIST introduces a novel deep learning algorithm, the Deep Response Model, which is a data-driven approach to discover multi-target transcriptomic profiles clinically linked to AUD. These targets can then be used to identify new treatments that have the potential to reduce addiction to alcohol and prevent relapse. State-of-the-art bioinformatics techniques rely on enrichment analysis tools that utilize imprecise knowledgebases, curated from unrelated diseases and model systems, and only cover a fraction of genes for which annotation exists. This leads to a low success rate in the identification of targets clinically linked to AUD. Instead, ASSIST will take a data-driven approach to learn higher level mappings between genes and their impact on disease state directly from transcriptomic data. Netrias will develop computational tests that will measure the accuracy, robustness, and specificity of the discovered targets to AUD. ASSIST will then integrate with drug databases to identify treatments that will reverse the expression profiles of the discovered set of targets.