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1. Novel Big Data-Driven Machine Learning Models for Drug Discovery Application

Author

Vishnu Sripriya Akondi, Vineetha Menon, Jerome Baudry, and Jana Whittle

Subjects
drug discovery, class imbalance, machine learning, protein conformation selecton, drug candidates, ADORA2A, Organic chemistry, QD241-441
Abstract

Most contemporary drug discovery projects start with a ‘hit discovery’ phase where small chemicals are identified that have the capacity to interact, in a chemical sense, with a protein target involved in a given disease. To assist and accelerate this initial drug discovery process, ’virtual docking calculations’ are routinely performed, where computational models of proteins and computational models of small chemicals are evaluated for their capacities to bind together. In cutting-edge, contemporary implementations of this process, several conformations of protein targets are independently assayed in parallel ‘ensemble docking’ calculations. Some of these protein conformations, a minority of them, will be capable of binding many chemicals, while other protein conformations, the majority of them, will not be able to do so. This fact that only some of the conformations accessible to a protein will be ’selected’ by chemicals is known as ’conformational selection’ process in biology. This work describes a machine learning approach to characterize and identify the properties of protein conformations that will be selected (i.e., bind to) chemicals, and classified as potential binding drug candidates, unlike the remaining non-binding drug candidate protein conformations. This work also addresses the class imbalance problem through advanced machine learning techniques that maximize the prediction rate of potential protein molecular conformations for the test case proteins ADORA2A (Adenosine A2a Receptor) and OPRK1 (Opioid Receptor Kappa 1), and subsequently reduces the failure rates and hastens the drug discovery process.

Published
2022
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3. Novel K-Means Clustering-based Undersampling and Feature Selection for Drug Discovery Applications

Author

Vineetha Menon, Jerome Baudry, Jana Whittle, and Vishnu Sripriya Akondi

Subjects
0303 health sciences, Class (computer programming), Process (engineering), Computer science, business.industry, Drug discovery, k-means clustering, Feature selection, 010402 general chemistry, Machine learning, computer.software_genre, 01 natural sciences, Pipeline (software), 0104 chemical sciences, 03 medical and health sciences, Identification (information), ComputingMethodologies_PATTERNRECOGNITION, Undersampling, Artificial intelligence, business, computer, 030304 developmental biology
Abstract

Drug discovery refers to the process of identification of specific-disease causing proteins and underscores the research efforts to derive a new medication that targets these proteins. As such the drug discovery process entails significant challenges as it is time consuming, data intensive, and involves an expensive developmental process which demands rigorous lab testing with high rates of uncertainty that the given drug will succeed. Therefore, it highlights the crucial need for machine learning methods to automate and hasten the drug discovery pipeline for improved healthcare and assist clinicians to make informed decisions for in-vitro testing. However, most real-world biomedical datasets suffer from statistical ill-conditioning issues such as the class imbalance problem where the fewer class of potential drug candidate protein conformations are overshadowed by the larger protein-pool of non-drug candidates. Hence, this leads to erroneous conclusions when machine learning techniques are directly employed for data-learning and classification purposes. Therefore, this work takes a revolutionary stance to counter the class imbalance problem through advanced machine learning techniques that maximize the prediction rate of potential drug candidate molecular conformations for the target proteins ADORA2A and OPRK1 and subsequently reduces the failure rates of the drug discovery process. Experimental evaluation of the proposed machine learning methodologies further substantiates the effectiveness of our approach for drug discovery process.

Published
2019

4. Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine

Author

Robert C. Green, Katrina A.B. Goddard, Gail P. Jarvik, Laura M. Amendola, Paul S. Appelbaum, Jonathan S. Berg, Barbara A. Bernhardt, Leslie G. Biesecker, Sawona Biswas, Carrie L. Blout, Kevin M. Bowling, Kyle B. Brothers, Wylie Burke, Charlisse F. Caga-anan, Arul M. Chinnaiyan, Wendy K. Chung, Ellen W. Clayton, Gregory M. Cooper, Kelly East, James P. Evans, Stephanie M. Fullerton, Levi A. Garraway, Jeremy R. Garrett, Stacy W. Gray, Gail E. Henderson, Lucia A. Hindorff, Ingrid A. Holm, Michelle Huckaby Lewis, Carolyn M. Hutter, Pasi A. Janne, Steven Joffe, David Kaufman, Bartha M. Knoppers, Barbara A. Koenig, Ian D. Krantz, Teri A. Manolio, Laurence McCullough, Jean McEwen, Amy McGuire, Donna Muzny, Richard M. Myers, Deborah A. Nickerson, Jeffrey Ou, Donald W. Parsons, Gloria M. Petersen, Sharon E. Plon, Heidi L. Rehm, J. Scott Roberts, Dan Robinson, Joseph S. Salama, Sarah Scollon, Richard R. Sharp, Brian Shirts, Nancy B. Spinner, Holly K. Tabor, Peter Tarczy-Hornoch, David L. Veenstra, Nikhil Wagle, Karen Weck, Benjamin S. Wilfond, Kirk Wilhelmsen, Susan M. Wolf, Julia Wynn, Joon-Ho Yu, Michelle Amaral, Laura Amendola, Samuel J. Aronson, Shubhangi Arora, Danielle R. Azzariti, Greg S. Barsh, E.M. Bebin, Barbara B. Biesecker, Brian L. Brown, Amber A. Burt, Peter H. Byers, Muge G. Calikoglu, Sara J. Carlson, Nizar Chahin, Kurt D. Christensen, Wendy Chung, Allison L. Cirino, Ellen Clayton, Laura K. Conlin, Greg M. Cooper, David R. Crosslin, James V. Davis, Kelly Davis, Matthew A. Deardorff, Batsal Devkota, Raymond De Vries, Pamela Diamond, Michael O. Dorschner, Noreen P. Dugan, Dmitry Dukhovny, Matthew C. Dulik, Kelly M. East, Edgar A. Rivera-Munoz, Barbara Evans, Jessica Everett, Nicole Exe, Zheng Fan, Lindsay Z. Feuerman, Kelly Filipski, Candice R. Finnila, Kristen Fishler, Bob Ghrundmeier, Karen Giles, Marian J. Gilmore, Zahra S. Girnary, Katrina Goddard, Steven Gonsalves, Adam S. Gordon, Michele C. Gornick, William M. Grady, David E. Gray, Robert Green, Robert S. Greenwood, Amanda M. Gutierrez, Paul Han, Ragan Hart, Patrick Heagerty, Naomi Hensman, Susan M. Hiatt, Patricia Himes, Fuki M. Hisama, Carolyn Y. Ho, Lily B. Hoffman-Andrews, Celine Hong, Martha J. Horike-Pyne, Sara Hull, Seema Jamal, Brian C. Jensen, Steve Joffe, Jennifer Johnston, Dean Karavite, Tia L. Kauffman, Dave Kaufman, Whitley Kelley, Jerry H. Kim, Christine Kirby, William Klein, Bartha Knoppers, Sek Won Kong, Ian Krantz, Joel B. Krier, Neil E. Lamb, Michele P. Lambert, Lan Q. Le, Matthew S. Lebo, Alexander Lee, Kaitlyn B. Lee, Niall Lennon, Michael C. Leo, Kathleen A. Leppig, Katie Lewis, Michelle Lewis, Neal I. Lindeman, Nicole Lockhart, Bob Lonigro, Edward J. Lose, Philip J. Lupo, Laura Lyman Rodriguez, Frances Lynch, Kalotina Machini, Calum MacRae, Daniel S. Marchuk, Josue N. Martinez, Aaron Masino, Heather M. McLaughlin, Carmit McMullen, Piotr A. Mieczkowski, Jeff Miller, Victoria A. Miller, Rajen Mody, Sean D. Mooney, Elizabeth G. Moore, Elissa Morris, Michael Murray, David Ng, Nelly M. Oliver, Will Parsons, Donald L. Patrick, Jeffrey Pennington, Denise L. Perry, Gloria Petersen, Sharon Plon, Katie Porter, Bradford C. Powell, Sumit Punj, Carmen Radecki Breitkopf, Robin A. Raesz-Martinez, Wendy H. Raskind, Dean A. Reigar, Jacob A. Reiss, Carla A. Rich, Carolyn Sue Richards, Christine Rini, Scott Roberts, Peggy D. Robertson, Jill O. Robinson, Marguerite E. Robinson, Myra I. Roche, Edward J. Romasko, Elisabeth A. Rosenthal, Joseph Salama, Maria I. Scarano, Jennifer Schneider, Christine E. Seidman, Bryce A. Seifert, Brian H. Shirts, Lynette M. Sholl, Javed Siddiqui, Elian Silverman, Shirley Simmons, Janae V. Simons, Debra Skinner, Elena Stoffel, Natasha T. Strande, Shamil Sunyaev, Virginia P. Sybert, Jennifer Taber, Deanne M. Taylor, Christian R. Tilley, Ashley Tomlinson, Susan Trinidad, Ellen Tsai, Peter Ubel, Eliezer M. Van Allen, Jason L. Vassy, Pankaj Vats, Victoria L. Vetter, Raymond D. Vries, Sarah A. Walser, Rebecca C. Walsh, Allison Werner-Lin, Jana Whittle, Ben Wilfond, Kirk C. Wilhelmsen, Yaping Yang, Carol Young, and Brian J. Zikmund-Fisher

Subjects
0301 basic medicine, Adult, Evidence-based practice, Biomedical Research, Best practice, Exploratory research, MEDLINE, Genomics, Computational biology, 030105 genetics & heredity, Bioinformatics, Polymorphism, Single Nucleotide, Article, 03 medical and health sciences, Population Groups, Genetics, Medicine, Genomic medicine, Humans, Genetics(clinical), Exome, Child, Genetics (clinical), Exome sequencing, Medical education, Clinical Trials as Topic, business.industry, Genome, Human, Correction, High-Throughput Nucleotide Sequencing, Human genetics, United States, 3. Good health, National Human Genome Research Institute (U.S.), 030104 developmental biology, Cardiovascular Diseases, Evidence-Based Practice, Human genome, business, Psychology, Software
Abstract

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine.

Published
2016

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