Algorithms for clustering data
Algorithms for clustering data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Multiobjective Genetic Algorithms for Clustering: Applications in Data Mining and Bioinformatics
Multiobjective Genetic Algorithms for Clustering: Applications in Data Mining and Bioinformatics
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
IEEE Transactions on Evolutionary Computation
Multiobjective GAs, quantitative indices, and pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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De novo ligand design involves optimization of several ligand properties such as binding affinity, ligand volume, drug likeness, etc. Therefore, optimization of these properties independently and simultaneously seems appropriate. In this paper, the ligand design problem is modeled in a multiobjective using Archived MultiObjective Simulated Annealing (AMOSA) as the underlying search algorithm. The multiple objectives considered are the energy components similarity to a known inhibitor and a novel drug likeliness measure based on Lipinski's rule of five. RecA protein of Mycobacterium tuberculosis, causative agent of tuberculosis, is taken as the target for the drug design. To gauge the goodness of the results, they are compared to the outputs of LigBuilder, NEWLEAD, and Variable genetic algorithm (VGA). The same problem has also been modeled using a well-established genetic algorithm-based multiobjective optimization technique, Nondominated Sorting Genetic Algorithm-II (NSGA-II), to find the efficacy of AMOSA through comparative analysis. Results demonstrate that while some small molecules designed by the proposed approach are remarkably similar to the known inhibitors of RecA, some new ones are discovered that may be potential candidates for novel lead molecules against tuberculosis.