This presentation highlights interdisciplinary applications of data science methods across protein engineering, drug discovery, and pathology. Case studies from protein design will show how computational modeling accelerates the design-build-test cycle. Examples from drug discovery will illustrate using machine learning to extract insights from chemical and biological data to streamline therapy development. Applications to pathology datasets will demonstrate how data integration and deep learning enable enhanced disease diagnosis and biomarker discovery. Common principles and challenges in applying data science will be discussed, providing perspectives into how data science drives scientific innovation in diverse fields.
Relevant References
A probabilistic view of protein stability, conformational specificity, and design., Stern JA, Free TJ, Stern KL, Gardiner S, Dalley NA, Bundy BC, Price JL, Wingate D, Della Corte D. Nature Scientific Reports, 2023
TrIP─Transformer Interatomic Potential Predicts Realistic Energy Surface Using Physical Bias, Bryce E. Hedelius, Damon Tingey, and Dennis Della Corte, Journal of Chemical Theory and Computation, 2024
MILCDock: Machine Learning Enhanced Consensus Docking for Virtual Screening in Drug Discovery, Connor J. Morris, Jacob A. Stern, Brenden Stark, Max Christopherson, and Dennis Della Corte, Journal of Chemical Information and Modeling, 2022
Don't fear the artificial intelligence: a systematic review of machine learning for prostate cancer detection in pathology, Frewing, A., Gibson, A. B., Robertson, R., Urie, P. M., & Della Corte, D., Archives of Pathology & Laboratory Medicine, 2024
Dr. Dennis Della Corte is an Associate Professor in the Physics and Astronomy Department at Brigham Young University and Chief Science Officer at ZONTAL, inc. He earned his Ph.D. in Physics from Research Center Jülich and Stanford University in 2015, along with M.S. degrees in Biomedical Engineering and Medical Physics. At BYU, Dr. Della Corte directs the interdisciplinary Consortium of Molecular Design focused on drug discovery, protein engineering, and laboratory automation. His research explores applications of deep learning and machine learning in these areas as well as augmented pathology. Prior to joining BYU in 2018, he held industry positions at Bayer Business Services GmbH. Dr. Della Corte has published over 25 peer-reviewed articles in leading journals such as Nature Communications, Drug Discovery Today, , and the Journal of Chemical Theory and Computation.
This presentation highlights interdisciplinary applications of data science methods across protein engineering, drug discovery, and pathology. Case studies from protein design will show how computational modeling accelerates the design-build-test cycle. Examples from drug discovery will illustrate using machine learning to extract insights from chemical and biological data to streamline therapy development. Applications to pathology datasets will demonstrate how data integration and deep learning enable enhanced disease diagnosis and biomarker discovery. Common principles and challenges in applying data science will be discussed, providing perspectives into how data science drives scientific innovation in diverse fields.
Relevant References
A probabilistic view of protein stability, conformational specificity, and design., Stern JA, Free TJ, Stern KL, Gardiner S, Dalley NA, Bundy BC, Price JL, Wingate D, Della Corte D. Nature Scientific Reports, 2023
TrIP─Transformer Interatomic Potential Predicts Realistic Energy Surface Using Physical Bias, Bryce E. Hedelius, Damon Tingey, and Dennis Della Corte, Journal of Chemical Theory and Computation, 2024
MILCDock: Machine Learning Enhanced Consensus Docking for Virtual Screening in Drug Discovery, Connor J. Morris, Jacob A. Stern, Brenden Stark, Max Christopherson, and Dennis Della Corte, Journal of Chemical Information and Modeling, 2022
Don't fear the artificial intelligence: a systematic review of machine learning for prostate cancer detection in pathology, Frewing, A., Gibson, A. B., Robertson, R., Urie, P. M., & Della Corte, D., Archives of Pathology & Laboratory Medicine, 2024
Dr. Dennis Della Corte is an Associate Professor in the Physics and Astronomy Department at Brigham Young University and Chief Science Officer at ZONTAL, inc. He earned his Ph.D. in Physics from Research Center Jülich and Stanford University in 2015, along with M.S. degrees in Biomedical Engineering and Medical Physics. At BYU, Dr. Della Corte directs the interdisciplinary Consortium of Molecular Design focused on drug discovery, protein engineering, and laboratory automation. His research explores applications of deep learning and machine learning in these areas as well as augmented pathology. Prior to joining BYU in 2018, he held industry positions at Bayer Business Services GmbH. Dr. Della Corte has published over 25 peer-reviewed articles in leading journals such as Nature Communications, Drug Discovery Today, , and the Journal of Chemical Theory and Computation.
