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<div class="" style="word-wrap:break-word"><span class="" style="font-size:14.666666984558105px">This is an announcement of Austin Clyde's Dissertation Defense.</span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">===============================================</span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Candidate: Austin Clyde</span><br class="" style="font-size:14.666666984558105px">
<br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Date: Thursday, October 27, 2022</span><br class="" style="font-size:14.666666984558105px">
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<span class="" style="font-size:14.666666984558105px">Time: 1 pm CST</span><br class="" style="font-size:14.666666984558105px">
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<span class="" style="font-size:14.666666984558105px">Location: JCL 390</span><br class="" style="font-size:14.666666984558105px">
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<span class="" style="font-size:14.666666984558105px">Title: Artificial Intelligence and High-Performance Computing for Accelerating Structure-Based Drug Discovery</span><br class="" style="font-size:14.666666984558105px">
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<span class="" style="font-size:14.666666984558105px">Abstract: Traditional techniques for discovering novel drugs are too slow for 21st challenges ranging from precision oncology to emerging global pandemics. The COVID-19 Pandemic demonstrated the unequivocal
need for rapidly deployable drug discovery capabilities as a matter of national biopreparedness and biosecurity. The challenge is, though, that drug discovery is an immense and complex interdisciplinary field drawing from cheminformatics, bioinformatics, biophysics,
machine learning, and high-performance computing among others. To accelerate the screening of new molecules, researchers are applying developments from artificial intelligence (AI) to the problem; however, the direct application of traditional AI methods overlooks
the essential complexities of drug discovery, ranging from protein-conformation flexibility to unique statistical properties of virtual ligand screening. This dissertation presents a new approach to AI for drug discovery based on tightly integrating insights
from biochemistry and biophysics, driving a more accurate and more interpretable drug discovery system, all while leveraging the same accelerating advances from AI. These cross-cutting contributions from AI and HPC workflows illustrate orders of magnitude
speedup for computational virtual drug screening, novel workflow designs for high-fidelity screening pipelines which are more accurate than traditional docking, new sampling strategies for exploring novel chemotypes, and complementary workflow analysis techniques
which directly links actionable and interpretable goals (such as the design of drugs) with quantitative cost functions. These methodological developments are realized in a case study discovering and validating a novel SARS-CoV-2 3CL-Main Protease inhibitor
with a Ki of 2.9mM (95% CI 2.2, 4.0).</span><br class="" style="font-size:14.666666984558105px">
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<span class="" style="font-size:14.666666984558105px">Advisors: Rick Stevens</span><br class="" style="font-size:14.666666984558105px">
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<span class="" style="font-size:14.666666984558105px">Committee Members: Rick Stevens, Eric Jonas, and Arvind Ramanathan</span><br class="" style="font-size:14.666666984558105px">
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