Many research efforts towards inhibiting the aggregation of Amyloid Beta peptide 42 in the brains of Alzheimer’s Disease patients have focused only on either laboratory or computational methodologies. The combination of lab experimentation and computational analysis, however, helps to make the research more substantive. During the summer of 2021, this research team paired data science and biochemistry research methods to test how well different small molecules inhibit the aggregation of Amyloid Beta 42. The combination of different disciplines enabled us to triangulate our research and test small molecules more efficiently. Our research was conducted as a multi-level screening, where each level further reduced the number of drug candidates. The first phase of the research involved collecting a database of drug candidates, which was followed by a virtual screening of those drug candidates. Then lab experimentation was performed with the reduced set of small molecules and a final drug candidate was identified. In addition to this, the data science researchers performed an exploratory analysis of Alzheimer’s Disease patient data (drug history, medical diagnoses and lab results) provided by the Alzheimer’s Disease Neuroimaging Initiative. The analysis was done to explore factors and drugs associated with Alzheimer’s disease. During this term, the researchers combined machine learning and statistical data analysis techniques, invivo and invitro experimentation, and molecular docking simulations to explore treatments for Alzheimer’s Disease. This method of analysis proved to be effective and yielded insights that will support future Alzheimer’s Disease research.