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UID:submissions.pasc-conference.org_PASC24_sess165@linklings.com
SUMMARY:MS2H - Updating Workflows in Virtual Drug Discovery with Current T
 echnologies
DESCRIPTION:Minisymposium\n\nDrug discovery is a difficult process that of
 ten relies on fortuitous discoveries. The number of such discoveries has s
 tagnated for decades despite numerous technological advances. The role of 
 HPC in the field is undergoing transformations due to the advent of large-
 scale machine learning models in recent years, which promise to revolution
 ize parts of the discovery pipeline. Traditionally, computation provides w
 ays to sample the mutual conformational space of ligands and receptors or 
 predicts physicochemical properties of small molecules, to name just two. 
 Our minisymposium zooms in on the following overarching considerations: fi
 rst, a mix of access to technology, computational resources, and data dict
 ates the ease and feasibility of use and therefore the widespread adoption
  of modern, AI-based prediction methods. Second, it is the particular chal
 lenge of complexes of small molecules and receptors that they pose many sp
 ecific problems but offer little generalizability. Third, it is difficult 
 to analyze results objectively when the ultimate goal is simply to discove
 r a new binder, which has limited the ability to transfer and abstract kno
 wledge. Following these lead concerns, our minisymposium is meant to foste
 r the exchange of technologies and to fortify the ongoing discourse on obj
 ectivity and standardization in computational drug discovery.\n\nLeveragin
 g Large Datasets to Assess the Potential of Machine Learning for Drug Targ
 et Prediction through Reverse Screening\n\nEstimating protein targets of c
 ompounds based on the similarity principle is a long-standing strategy in 
 drug discovery. Building upon prior quantification of this principle, the 
 large-scale assessment of its predictive power was performed using an unpr
 ecedented vast external test set of more than 3...\n\n\nVincent Zoete (Uni
 versity of Lausanne)\n---------------------\nMolecular Docking and Virtual
  Screening at Scale with GNINA\n\nMolecular docking computationally predic
 ts the conformation of a small molecule when binding to a receptor. Scorin
 g functions are a vital piece of any molecular docking pipeline as they de
 termine the fitness of sampled poses.\nWe will describe the training and d
 evelopment of convolutional neural netw...\n\n\nDavid Koes (University of 
 Pittsburgh)\n---------------------\nAn Integrated, HPC-Ready, Graphical Pl
 atform to Discover, Test, and Refine Small Molecule Binders\n\nIn virtual 
 drug discovery, the reproducibility of results across different practition
 ers is a frequent concern, and the roles of human intervention, e.g., thro
 ugh visual inspection, are hard to quantify and recapitulate. This is in p
 art a result of the nature of the task, which is to find, rather th...\n\n
 \nYang Zhang (University of Zurich)\n---------------------\nGPU-Accelerate
 d Molecular Dynamics Simulations for Multistate Binding Affinity Calculati
 ons with RE-EDS\n\nComputational approaches for estimating protein-ligand 
 binding affinities are crucial in modern drug discovery. All-atom explicit
 -solvent molecular dynamics (MD) simulations use the foundational principl
 es of classical mechanics and statistical thermodynamics to rigorously cal
 culate binding free ene...\n\n\nEnrico Ruijsenaars (ETH Zurich)\n\nDomain:
  Chemistry and Materials, Life Sciences\n\nSession Chair: Andreas Vitalis 
 (University of Zurich)
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