Computational Polypharmacology: A Machine Learning Approach
Dr. Sally R. Ellingson
Abstract: Drug discovery is a lengthy, expensive, and sometimes fatal process. It is also an extremely difficult task to perform with a full understanding of experimental results. Drugs are studied in test tubes which lack a realistic in vivo environment and in animal models having limited validity for human conditions. Computational methods that consistently improve predictive accuracy over laboratory and animal testing for the entire human proteome and huge chemical space of potential drugs could revolutionize pharmaceutical research and development. The utilization of such computational tools will increase the return on future investments in health-related research and provide access to new, better understood therapies.
The state-of-the-art in many computational methodologies include machine learning approaches. In our digitalized, data-driven world, there is a wealth of knowledge available that is beyond the processing power of an individual researcher or even team of researchers. The goal of my work is to improve the prediction of novel drug safety and efficacy by increasing the accuracy of predicting polypharmacological networks, investigating how drugs interact with the entire proteome. We integrate traditional computational simulations of protein and drug interactions (such as the efficient molecular docking calculation), cheminformatics features of drug-like molecules, and features describing individual proteins to improve the prediction of drug and protein binding. Each component investigated provides some level of predictive utility in isolation. These same drug features have been used in machine learning models in combination with docking scores to rescore interactions with one candidate drug to multiple proteins. The individual components of a molecular docking scoring function can be used as features in a machine learning model to greatly improve the accuracy of identifying active compounds in models specific for one protein. Presented here is a first step of showing that it can be done for a class of functionally related proteins (kinases).
Biography: Dr. Sally Ellingson is a computational scientist working at the intersection of computational biology and high-performance computing. She has undergraduate degrees in computer science and mathematics from Florida Institute of Technology. She obtained her doctoral degree at the University of Tennessee and Oak Ridge National Laboratory under a fellowship funded by the National Science Foundation in computational biology. She is an assistant professor in the Division of Biomedical Informatics at the University of Kentucky College of Medicine. In her additional role as the manager for High-Performance Computing Services for the Markey Cancer Center’s Cancer Research Informatics Shared Resource Facility she facilitates high throughput genomics and big data processing for precision medicine resulting in targeted cancer therapies. Dr. Ellingson is currently a KL2 scholar awarded by the University of Kentucky Center for Clinical and Translational Sciences and a Research Affiliate at Lawrence Berkeley National Laboratory. With her passion for high-performance computing, her research goals lie in harnessing computational power for discoveries otherwise not possible in biomedical areas of high societal importance.
Dr. Ellingson engages in mentoring and outreach, especially for underrepresented groups in computational sciences. Since graduation she has been actively engaged in the organization of the student programs at Supercomputing, the largest conference in high-performance computing. She is the main chair for the student volunteer program this year and keeps the diversity of program participants a priority. She also organizes and volunteers for the Broader Engagement program at the Society for Industrial and Applied Mathematics: Computational Sciences and Engineering meeting.