License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.CP.2023.4
URN: urn:nbn:de:0030-drops-190415
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/19041/
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Schiex, Thomas

Coupling CP with Deep Learning for Molecular Design and SARS-CoV2 Variants Exploration (Invited Talk)

pdf-format:
LIPIcs-CP-2023-4.pdf (0.4 MB)

Abstract

The use of discrete optimization, including Constraint Programming, for designing objects that we completely understand is quite usual. In this talk, I'll show how designing specific biomolecules (proteins) raises new challenges, requiring solving problems that combine precise design targets, approximate laws, and design rules that can be deep-learned from data.

BibTeX - Entry

@InProceedings{schiex:LIPIcs.CP.2023.4,
  author =	{Schiex, Thomas},
  title =	{{Coupling CP with Deep Learning for Molecular Design and SARS-CoV2 Variants Exploration}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{4:1--4:3},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/19041},
  URN =		{urn:nbn:de:0030-drops-190415},
  doi =		{10.4230/LIPIcs.CP.2023.4},
  annote =	{Keywords: graphical models, deep learning, constraint programming, cost function networks, random Markov fields, decision-focused learning, protein design}
}

Keywords: graphical models, deep learning, constraint programming, cost function networks, random Markov fields, decision-focused learning, protein design
Collection: 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)
Issue Date: 2023
Date of publication: 22.09.2023

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