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    Advancing Simulation Methods for Molecular Design and Drug Discovery

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    Genre
    Thesis/Dissertation
    Date
    2022
    Author
    Hurley, Matthew cc
    Advisor
    Voelz, Vincent
    Committee member
    Schafmeister, Christian
    Matsika, Spiridoula
    Carnevale, Vincenzo
    Department
    Chemistry
    Subject
    Computational chemistry
    Biophysics
    Molecular biology
    Drug discovery
    Free energy calculations
    Molecular dynamics
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/7990
    
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    DOI
    http://dx.doi.org/10.34944/dspace/7962
    Abstract
    Investigating interactions between proteins and small molecules at an atomic scale is fundamental towards understanding biological processes and designing novel candidates during the pre-clinical stages of drug discovery. By optimizing the methods used to study these interactions in terms of accuracy and computational cost, we can accelerate this aspect of biological research and contribute more readily to therapeutic design. While biological assays and other experimental techniques are invaluable in quantitatively determining in vitro and in vivo inhibition activity, as well as validating computational predictions, there is an inherent benefit in the possible throughput provided by molecular dynamics (MD) simulations and related computational methods. These calculations provide researchers with unparalleled access to large amounts of all-atom sampling of biological systems, including non-physical pathways and other enhanced sampling methods. This dissertation presents research into advancing the application of expanded ensemble and other simulation-based methods of ligand design towards reliable and efficient absolute free energy of binding calculations on the scale of hundreds to thousands of small molecule ligands. This culminates in a combined workflow that allows for an automated approach to the force-field parameterization of custom systems, simulation preparation, optimization of the restraint and sampling protocols, production free energy simulations, and analysis that has facilitated the computation of absolute binding free energy predictions. Specifically highlighted is our ongoing effort to discover novel inhibitors of the main protease (Mpro) of SARS-CoV-2 as well as participation in the SAMPL9 Host-Guest Challenge.
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