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DTSTAMP:20241120T082409Z
LOCATION:HG F 26.5
DTSTART;TZID=Europe/Stockholm:20240603T143000
DTEND;TZID=Europe/Stockholm:20240603T150000
UID:submissions.pasc-conference.org_PASC24_sess165_msa111@linklings.com
SUMMARY:Molecular Docking and Virtual Screening at Scale with GNINA
DESCRIPTION:Minisymposium\n\nDavid Koes (University of Pittsburgh)\n\nMole
 cular docking computationally predicts the conformation of a small molecul
 e when binding to a receptor. Scoring functions are a vital piece of any m
 olecular docking pipeline as they determine the fitness of sampled poses.\
 nWe will describe the training and development of convolutional neural net
 works for protein-ligand scoring and how these deep learning models are in
 tegrated into the GNINA molecular docking open source software.   We will 
 describe the role high performance computing played in the training and op
 timzation of these networks and how high throughput docking can be perform
 ed at scale. Successful prospective evaluations of GNINA will be discussed
 , including recent top performance in the Critical Assessment of Computati
 onal Hit-Finding Experiments (CACHE).\n\nDomain: Chemistry and Materials, 
 Life Sciences\n\nSession Chair: Andreas Vitalis (University of Zurich)
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