Coupling CP with Deep Learning for Molecular Design and SARS-CoV2 Variants Exploration
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.
Keywords
graphical models
deep learning
constraint programming
cost function networks
random Markov fields
decision-focused learning
protein design
Computing methodologies → Artificial intelligence
Computing methodologies → Machine learning
Theory of computation → Constraint and logic programming
Computing methodologies → Learning in probabilistic graphical models
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
2012 ACM Subject Classification Computing methodologies → Artificial intelligence
Computing methodologies → Learning graphical models, deep learning, constraint programming, cost function networks, random Markov fields, decision-focused learning, protein design .CP.2023
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