Fundamental properties of neighbourhood substitution in constraint satisfaction problems
Artificial Intelligence
Reversible DAC and other improvements for solving Max-CSP
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Arc Consistency for Soft Constraints
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
A CLP Approach to the Protein Side-Chain Placement Problem
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Directed Arc Consistency Preprocessing
Constraint Processing, Selected Papers
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Existential arc consistency: getting closer to full arc consistency in weighted CSPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Soft arc consistency revisited
Artificial Intelligence
Interchangeability in soft CSPs
ERCIM'02/CologNet'02 Proceedings of the 2002 Joint ERCIM/CologNet international conference on Constraint solving and constraint logic programming
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Proteins are chains of simple molecules called amino acids. The three-dimensional shape of a protein and its amino acid composition define its biological function. Over millions of years, living organisms have evolved and produced a large catalog of proteins. By exploring the space of possible amino-acid sequences, protein engineering aims at similarly designing tailored proteins with specific desirable properties. In Computational Protein Design (CPD), the challenge of identifying a protein that performs a given task is defined as the combinatorial optimization problem of a complex energy function over amino acid sequences. In this paper, we introduce the CPD problem and some of the main approaches that have been used to solve it. We then show how this problem directly reduces to Cost Function Network (CFN) and 0/1LP optimization problems. We construct different real CPD instances to evaluate CFN and 0/1LP algorithms as implemented in the toulbar2 and cplex solvers. We observe that CFN algorithms bring important speedups compared to the CPD platform osprey but also to cplex.