This I-Corps project focuses on an advanced drug discovery platform that employs artificial intelligence and multiscale simulations to identify promising drug candidates at a fraction of the cost of existing methods. This technology addresses the core challenge of extremely slow, expensive, and prone-to-failure drug discovery processes. The technology cuts costs and speeds up candidate optimization by running three parallel calculations simultaneously in a single and unified computational workflow. Unifying these calculations into a single workflow can screen thousands of potential drugs in a fraction of the time and reduce reliance on lab experiments. Faster identification of effective therapies improves patient outcomes, lowers healthcare costs, and enhances the ability of the nation to respond to emerging health threats. These societal and economic benefits extend to academic institutions, research universities, and biotechnology companies by facilitating cross‐disciplinary collaboration for accelerated drug discovery campaigns. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of an automated, artificial intelligence-driven, multiscale, end-to-end drug discovery pipeline that integrates high-throughput virtual screening of candidates, quantum mechanical refinement to accurately model drug-target interaction