Search
Now showing items 1-10 of 12
Article
Solving molecular flexible docking problems with metaheuristics: A comparative study
(Elsevier, 2015)
The main objective of the molecular docking problem is to find a conformation between a small molecule (ligand) and a receptor molecule with minimum binding energy. The quality of the docking score depends on two factors: ...
Article
BIGOWL: Knowledge centered Big Data analytics
(Elsevier, 2019)
Knowledge extraction and incorporation is currently considered to be beneficial for efficient Big Data an- alytics. Knowledge can take part in workflow design, constraint definition, parameter selection and con- figuration, ...
Article
Bio-inspired optimization for the molecular docking problem: State of the art, recent results and perspectives
(Elsevier, 2019)
Molecular docking is a Bioinformatics method based on predicting the position and orientation of a small molecule or ligand when it is bound to a target macromolecule. This method can be modeled as an optimization problem ...
Article
Solving Molecular Docking Problems with Multi-Objective Metaheuristics
(MDPI, 2015)
Molecular docking is a hard optimization problem that has been tackled in the past with metaheuristics, demonstrating new and challenging results when looking for one objective: the minimum binding energy. However, only a ...
Article
Multi-objective ligand-protein docking with particle swarm optimizers
(Elsevier, 2019)
In the last years, particle swarm optimizers have emerged as prominent search methods to solve the molecular docking problem. A new approach to address this problem consists in a multi-objective formulation, minimizing the ...
Article
Molecular Docking Optimization in the Context of Multi-Drug Resistant and Sensitive EGFR Mutants
(MDPI, 2016)
The human Epidermal Growth Factor (EGFR) plays an important role in signaling pathways, such as cell proliferation and migration. Mutations like G719S, L858R, T790M, G719S/T790M or T790M/L858R can alter its conformation, ...
Article
InDM2: Interactive Dynamic Multi-Objective Decision Making Using Evolutionary Algorithms
(Elsevier, 2018)
Dynamic optimization problems involving two or more conflicting objectives appear in many real-world scenarios, and more cases are expected to appear in the near future with the increasing interest in the analysis ...
Article
M2Align: parallel multiple sequence alignment with a multi-objective metaheuristic
(Oxford University Press, 2017)
Motivation: Multiple sequence alignment (MSA) is an NP-complete optimization problem found in computational biology, where the time complexity of finding an optimal alignment raises exponen-tially along with the number ...
Article
jMetalSP: A framework for dynamic multi-objective big data optimization
(Elsevier, 2018)
Multi-objective metaheuristics have become popular techniques for dealing with complex optimization problems composed of a number of conflicting functions. Nowadays, we are in the Big Data era, so metaheuristics must be ...
Article
A Study of Multiobjective Metaheuristics When Solving Parameter Scalable Problems
(IEEE Computer Society, 2010)
To evaluate the search capabilities of a multiobjective algorithm, the usual approach is to choose a benchmark of known problems, to perform a fixed number of function evaluations, and to apply a set of quality indicators. ...