Journal of Chemical Information & Computer Sciences
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Enhancing Decision Space Diversity in Evolutionary Multiobjective Algorithms
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Combining Aggregation with Pareto Optimization: A Case Study in Evolutionary Molecular Design
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Enhancing search space diversity in multi-objective evolutionary drug molecule design using niching
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A dynamic data structure for flexible molecular maintenance and informatics
2009 SIAM/ACM Joint Conference on Geometric and Physical Modeling
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This paper presents an evolutionary algorithm for the automated design of molecules that could be used as drugs. It is designed to provide the medicinal chemist with a number of candidate molecules that comply to pre-defined properties. These candidate molecules can be promising for further evaluation. The proposed algorithm is implemented as an extension to the so-called Molecule Evoluator [3] which implements an interactive evolutionary algorithm. The Molecule Evoluator is extended with an automated evolutionary algorithm that implements a variable sized population and bases its search on target-bounds that are set for a number of molecule properties. Moreover, the algorithm uses a selection procedure based on the notion of Pareto domination. The results show that it is indeed possible to apply the concept of evolutionary computation on automated molecule design using target-bounds for molecule properties as optimization goals. For practical usage, the presented algorithm could serve as a starting point, but should be further improved with respect to diversity within the generated set of molecules.