Get the latest news
Subscribe to receive updates from the School of Data Science.
Lecturer
Peter Gedeck
Lecturer
School of Data Science
Email: tux3md@virginia.edu
Additional Contact Information
Website: www.linkedin.com/in/petergedeck/
Pronouns: he/him
Peter Gedeck is a Lecturer at the School of Data Science. His research interests include cheminformatics, research and development, life sciences, and molecular modeling. He has expertise in the intersection of data science and the pharmaceutical industry.
Gedeck is also the Research Informatics Senior Scientist at Collaborative Drug Discovery, where he develops useful, production quality drug discovery software and creates novel cheminformatics approaches for drug discovery.
He has over 50 peer-reviewed publications and co-authored several books:
- Practical Statistics for Data Scientists
- Data Mining for Business Analytics: Concepts, Techniques, and Applications
- Machine Learning for Business Analytics (R and Python versions)
- Modern Statistics: A Computer Based Approach with Python
- Industrial Statistics: A Computer Based Approach with Python
- Statistics for Data Science and Analytics
Peter Gedeck holds a Ph.D. in Chemistry from FAU Erlangen Nürnberg.
Education
- Ph.D., Chemistry, FAU Erlangen Nürnberg
Data Science Domains
Analytics
Areas of Practice
Artificial Intelligence and Machine Learning; Biomedical, Health, and Life Sciences
Methodologies
Prediction Modeling; Cheminformatics
Selected Publications
Galit Shmueli, Peter C. Bruce, Peter Gedeck, and Nitin R. Patel. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python (Second Edition) John Wiley & Sons, Inc. (2025: ISBN: 978-1-394-28679-9) and in R (2nd Edition) John Wiley & Sons, Inc. (2023: ISBN: 978-1-119-83517-2)
Peter C. Bruce, Peter Gedeck, Janet Dobbins. Statistics for Data Science and Analytics. John Wiley & Sons, Inc. (2024: ISBN: 978-1-394-25381-4)
Ron Kenett, Shelemyahu Zacks, Peter Gedeck. Modern Statistics: A Computer Based Approach with Python. Springer International Publishing; 1st edition (2022: ISBN: 978-3-031-07565-0)
Ron Kenett, Shelemyahu Zacks, Peter Gedeck. Industrial Statistics: A Computer Based Approach with Python. Springer Birkhäuser, 1st edition (2023:ISBN 978-3-031-28481-6)
Clark Alex Michael, Gedeck Peter, Cheung Philip P., Bunin Barry A. Using machine learning to parse chemical mixture descriptions. ACS Omega 6 (2021) 22400-22409. [DOI: 10.1021/acsomega.1c03311]
Lu Yipin, Anand Shankara, Shirley Bill, Gedeck Peter, Kelley Brian, Skolnik Suzanne, Rodde Stephane, Nguyen Mai, Lindvall Mika, Jia Weiping. Prediction of pKa Using Machine Learning Methods with Rooted Topological Torsion Fingerprints: Application to Aliphatic Amines. J Chem Inf Model 59 (2019), 4706-4719. [DOI:10.1021/acs.jcim.9b00498]
Clark Alex Michael, McEwen Leah Rae, Gedeck Peter, Bunin Barry Arthur. Capturing mixture composition: an open machine-readable format for representing mixed substances. J Cheminform 11 (2019) 33. [DOI:10.1186/s13321-019-0357-4]
Alexander Chao, Boon Heng Lee, Wan Kah Fei, Jeremy Selva, Zou Bin, Peter Gedeck, David Beer, Thierry Diagana, Ghislain Bonamy, Ujjini Manjunatha. Development of a novel cytopathic effect-based phenotypic screening assay against Cryptosporidium. ACS Infectious Diseases 4 (2018) 635-645. [DOI:10.1021/acsinfecdis.7b00247]
Peter Gedeck, Suzanne Skolnik, Stephane Rodde. Developing collaborative QSAR models without sharing structures. J Chem Inf Model 57 (2017) 1847-1858. [DOI: 10.1021/acs.jcim.7b00315]
Manjunatha Ujjini H, Vinayak Sumiti, Zambriski Jennifer A, Chao Alexander T, Sy Tracy, Noble Christian G, Bonamy Ghislain MC, Kondreddi Ravinder R, Zou Bin, Gedeck Peter, Brooks Carrie F, Herbert Gillian T, Sateriale Adam, Tandel Jayesh, Noh Susan, Lakshminarayana Suresh B, Lim Siau H, Goodman Laura B, Bodenreider Christophe, Feng Gu, Zhang Lijun, Blasco Francesca, Wagner Juergen, Leong F. Joel, Striepen Boris, Diagana Thierry T. An
inhibitor of the Cryptosporidium PI(4)K is a drug candidate for the treatment of pediatric cryptosporidiosis. Nature 546 (2017) 376-380. [DOI:10.1038/nature22337]