Coupling CP with Deep Learning for Molecular Design and SARS-CoV2 Variants Exploration
Coupling CP with Deep Learning for Molecular Design and SARS-CoV2 Variants Exploration
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.
random Markov fields, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], constraint programming, Computing methodologies → Learning graphical models, deep learning, constraint programming, cost function networks, random Markov fields, decision-focused learning, protein design .CP.2023, decision-focused learning, 2012 ACM Subject Classification Computing methodologies → Artificial intelligence, deep learning, 2012 ACM Subject Classification Computing methodologies → Artificial intelligence Computing methodologies → Machine learning Theory of computation → Constraint and logic programming Computing methodologies → Learning graphical models, deep learning, constraint programming, cost function networks, random Markov fields, decision-focused learning, protein design .CP.2023, [INFO] Computer Science [cs], Computing methodologies → Artificial intelligence, [INFO.INFO-BT] Computer Science [cs]/Biotechnology, Computing methodologies → Machine learning, Computing methodologies → Learning in probabilistic graphical models, Theory of computation → Constraint and logic programming, cost function networks, graphical models, protein design, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
random Markov fields, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], constraint programming, Computing methodologies → Learning graphical models, deep learning, constraint programming, cost function networks, random Markov fields, decision-focused learning, protein design .CP.2023, decision-focused learning, 2012 ACM Subject Classification Computing methodologies → Artificial intelligence, deep learning, 2012 ACM Subject Classification Computing methodologies → Artificial intelligence Computing methodologies → Machine learning Theory of computation → Constraint and logic programming Computing methodologies → Learning graphical models, deep learning, constraint programming, cost function networks, random Markov fields, decision-focused learning, protein design .CP.2023, [INFO] Computer Science [cs], Computing methodologies → Artificial intelligence, [INFO.INFO-BT] Computer Science [cs]/Biotechnology, Computing methodologies → Machine learning, Computing methodologies → Learning in probabilistic graphical models, Theory of computation → Constraint and logic programming, cost function networks, graphical models, protein design, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!