Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Concurrency and Computation: Practice & Experience - Third IEEE International Workshop on High Performance Computational Biology (HiCOMB 2004)
Information Characteristics and the Structure of Landscapes
Evolutionary Computation
Multiobjective Optimization in Bioinformatics and Computational Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Enhancing search space diversity in multi-objective evolutionary drug molecule design using niching
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Bioscientific data processing and modeling
ISoLA'12 Proceedings of the 5th international conference on Leveraging Applications of Formal Methods, Verification and Validation: applications and case studies - Volume Part II
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Computer simulation techniques are being used extensively in the pharmaceutical field to model protein-ligand and protein-protein interactions; however, few procedures have been established yet for the design of ligands from scratch ('de novo'). To improve upon the current state, in this work the problem of finding a peptide ligand was formulated as a bi-objective optimization problem and a state-of-the-art algorithm for evolutionary multiobjective optimization, namely SMS-EMOA, has been employed for exploring the search space. This algorithm is tailored to this problem class and used to produce a Pareto front in high-dimensional space, here consisting of 2322 or about 1030 possible solutions. From the knee point of the Pareto front we were able to select a ligand with preferential binding to the gamma versus the epsilon isoform of the Danio rerio (zebrafish) 14-3-3 protein. Despite the high-dimensional space the optimization algorithm is able to identify a 22-mer peptide ligand with a predicted difference in binding energy of 291 kcal/mol between the isoforms, showing that multiobjective optimization can be successfully employed in selective ligand design.