Development of a Computationally‐Based Medicinal Chemistry Course at a Small, Primarily Undergraduate Institution

Computational analysis and molecular modeling are essential tools in many disciplines in modern chemistry, and undergraduate students benefit from exposure to these methods. Described here is the development of an active learning Medicinal Chemistry course with an extensive computational component. The course was targeted at upper‐division Chemistry and Biology majors, all of whom had already taken Biochemistry. A Team‐Based Learning (TBL) format was chosen for the course, to leverage both active learning strategies and the power of group learning for the computational work. Overall, the course emphasized the basic chemical and biological principles of Medicinal Chemistry, the process of drug discovery and optimization as well as the power and limitations of computational methods in drug discovery. Software utilized included Marvin Suite, AutoDock, and AutoGrid, as these are all free for academic use. The computers were machines re‐purposed from a computer lab on campus, and had no high‐end processors or other add‐on components. Course details will be discussed, including specific topics and a series of scaffolded molecular modeling exercises utilized in class. Ultimately, students used the content learned in the course to complete an in silico modification of an existing drug, and to predict the effectiveness of the new drug on the original target. Although only limited computational resources were available, student feedback indicates that Medicinal Chemistry was a challenging but rewarding addition to our curriculum. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .