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DTSTART;TZID=America/New_York:20241118T161500
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UID:submissions.supercomputing.org_SC24_sess818_ws_cafcws106@linklings.com
SUMMARY:Generalized Generative Molecular Design: An open source, Modular T
 ool for De Novo Drug Design
DESCRIPTION:Eli Brignac (University of Delaware) and Sean Black (Frederick
  National Laboratory for Cancer Research)\n\nThe chemical space of small m
 olecules is vast, making de novo drug design challenging. Traditional meth
 ods are slow and costly. While AI advancements have improved this process,
  we still face limitations in exploring the larger chemical space. In onco
 logical drug discovery, various factors such as selectivity, efficacy, saf
 ety, toxicity, and synthesizability must be considered.\n\nWe introduce th
 e Generalized Generative Molecular Design (GGMD), an open-source tool that
  combines generative AI with population-based optimization algorithms for 
 drug design and lead optimization. GGMD’s modular and customizable framewo
 rk allows users to adjust methods to fit specific research needs, balancin
 g trade-offs like efficacy and synthesizability. Designed for accessibilit
 y, GGMD is transportable and provides tools for visualizing results and re
 fining parameters.\n\nWe’ve successfully used GGMD to optimize properties 
 such as LogP and toxicity, leading to the discovery of new molecules.\n\nT
 ag: Artificial Intelligence/Machine Learning, Biology, Education, Emerging
  Technologies, Medicine, Modeling and Simulation\n\nRegistration Category:
  Workshop Reg Pass\n\nSession Chairs: Lynn Borkon (Frederick National Labo
 ratory for Cancer Research); Lauren Lewis (Frederick National Laboratory f
 or Cancer Research); and Eric Stahlberg (MD Anderson Cancer Center, Univer
 sity of Texas)\n\n
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