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SUMMARY:AI in chemistry and beyond: Molecular Generative Models: Diffusion
  for 3D Geometry Generation
DTSTART:20230502T173000
DTEND:20230502T183000
DTSTAMP:20261001T181319Z
UID:e8f2cb7f957f3fabd3aef414581b2c86852743f3b30a97ff1ecb0045
CATEGORIES:Conferences - Seminars
DESCRIPTION:Minkai Xu is a Ph.D. student in the Computer Science Departmen
 t at Stanford University. Previously\, he received his M.Sc degree from Mi
 la and B.E. from Shanghai Jiaotong University. His research lies in probab
 ilistic models\, geometric representation learning\, and ML for scientific
  discovery. He has published several influential papers on the above topic
 s in top machine learning conferences (e.g.\, ICML\, NeurIPS\, ICLR\, AAAI
 \, and AAMAS) including the first diffusion models for molecular structure
  generation\, which has been widely adopted in various drug and protein de
 sign problems. His research is generously supported by Sequoia Capital Sta
 nford Graduate Fellowship.\nWith the recent progress in geometric deep lea
 rning\, generative modeling\, and the availability of large-scale biologic
 al datasets\, molecular geometry generative modeling has emerged as a high
 ly promising direction for scientific discovery such as drug design. These
  generative methods enable efficient chemical space exploration and potent
 ial drug candidate generation. However\, by representing molecules as 3D g
 eometries\, there exist many both fundamental and challenging problems for
  modeling the distribution of these irregular and complex relational data.
  In this talk\, we will introduce the latest key developments in this fiel
 d\, covering important principles for designing  3D molecular geometry ge
 neration including our most recent diffusion generative models. We will ou
 tline the underlying problem characteristics\, summarize key challenges\, 
 present unified views of the representative approaches\, and highlight fut
 ure research direction and potential impacts.
LOCATION:https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYl
 NRdz09
STATUS:CONFIRMED
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