BEGIN:VCALENDAR
VERSION:2.0
PRODID:researchseminars.org
CALSCALE:GREGORIAN
X-WR-CALNAME:researchseminars.org
BEGIN:VEVENT
SUMMARY:Guo-Wei Wei (Michigan State University)
DTSTART:20200617T150000Z
DTEND:20200617T160000Z
DTSTAMP:20260423T005838Z
UID:GSMMA/7
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/GSMMA/7/">Ma
 thematical AI for drug discovery</a>\nby Guo-Wei Wei (Michigan State Unive
 rsity) as part of Global Seminar on Mathematical Modeling and Applications
 \n\n\nAbstract\nArtificial intelligence (AI) has fundamentally changed the
  landscape of science\, technology\, industry\, and social media in the pa
 st few years. It holds a great future for discovering new drugs significan
 tly faster and cheaper. However\, AI-based drug discovery encounters obsta
 cles arising from the structural complexity of protein-drug interactions a
 nd the high dimensionality of drug candidates’ chemical space. We tackle
  these challenges mathematically. Our work focuses on reducing the biomole
 cular complexity and dimensionality in AI. We have introduced evolutionary
  de Rham-Hodge\, algebraic topology\, and persistent spectral graph theory
  to obtain high-level abstractions of protein-drug interactions and thus s
 ignificantly enhance AI's ability to handle excessively large datasets of 
 complex biomolecules in drug discovery.  Using our mathematical AI approac
 h\, my team has been a top winner in D3R Grand Challenges\, a worldwide an
 nual competition series in computer-aided drug design and discovery in the
  past three years. I will briefly discuss Math and AI-based drug repositio
 ning for COVID-19.\n\nthe meeting ended\n
LOCATION:https://researchseminars.org/talk/GSMMA/7/
END:VEVENT
END:VCALENDAR