Coupling CP with Deep Learning for Molecular Design and SARS-CoV2 Variants Exploration
Résumé
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.
Mots clés
- 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
- 2012 ACM Subject Classification Computing methodologies → Artificial intelligence
- 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
- Computing methodologies → Learning in probabilistic graphical models
- Computing methodologies → Machine learning
- Computing methodologies → Artificial intelligence
- protein design
- decision-focused learning
- random Markov fields
- cost function networks
- constraint programming
- deep learning
- graphical models
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