Bryce Allen received his PhD from the University of Miami Miller School of Medicine under the supervision of Dr. Stephan Schürer and Dr. Nagi Ayad. Allen’s doctoral research focused on large-scale computational screening and machine learning approaches to drug discovery, where he designed, tested and validated a large-scale high-throughput computational screening pipeline to discover novel multitarget inhibitors in kinase and epigenetic signaling space. This pipeline integrated ligand and structure-based models to prioritize chemical scaffolds using a combination of Bayesian statistics, docking, and molecular dynamics simulations. Additionally, he was a member of the Data Coordination and Integration Center for the Library of Integrated Network-based Cellular Signatures (LINCS), where he helped standardize and process chemical entities used in LINCS assays. Since coming to DBMI, he has focused on developing software to aid in therapeutic prioritization using large volumes of patient data. Taking a data-driven approach, Allen is integrating chemical, genomic, and proteomic datasets to learn actionable therapeutic insights from orthogonal sources using machine learning and molecular modeling, with the aim to improve drug repurposing/discovery efforts and ultimately patient outcomes.
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