Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Algorithms in Molecular Design
Evolutionary Algorithms in Molecular Design
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Variable genetic operator search for the molecular docking problem
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Molecular docking with opposition-based differential evolution
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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The pharmaceutical industry is facing an ever-increasing demand to discover novel drugs that are more effective and safer than existing ones. The industry faces huge problem in improving its drug discovery and development processes since formerly used methods have shown their limits. Additionally, tests for safety of drugs are performed at the later end of the drug discovery pipeline instead of earlier. Therefore, the industry is looking for predictive tools that would be useful in testing the behaviour of a drug candidate earlier on in the pipeline before performing the large scale clinical tests. This paper explores the application of evolutionary multi-objective optimisation techniques for achieving such predictive work in protein-ligand docking. The paper reviews the literature of multi-objective optimisation and the drug discovery process and proposes a framework as a predictive tool to calculate good docking configuration for a given target protein and its binding compound. Finally existing models for drug evaluation are used for framework validation.