jMetalCpp: optimizing molecular docking problems with a C++ metaheuristic framework

López-Camacho, Esteban; García-Godoy, María Jesús; Nebro, Antonio J.; Aldana‐Montes, José F.

doi:10.1093/bioinformatics/btt679

Cited by 28 publications

(19 citation statements)

References 13 publications

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“…We have used the implementation of the five studied algorithms provided in the jMetalCpp framework [7], in combination with AutoDock 4.2 to evaluate the new generated solutions. To cope with the high computational requirements Each mutation is applied to 1/3 of the swarm needed to carry out all the experiments, we have used the Condor 4 system, a middleware platform acting a distributed task scheduler of up to 400 cores.…”

Section: Methodsmentioning

confidence: 99%

A Study of Archiving Strategies in Multi-objective PSO for Molecular Docking

García-Nieto

López-Camacho

García-Godoy

et al. 2016

Lecture Notes in Computer Science

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Abstract. Molecular docking is a complex optimization problem aimed at predicting the position of a ligand molecule in the active site of a receptor with the lowest binding energy. This problem can be formulated as a bi-objective optimization problem by minimizing the binding energy and the Root Mean Square Deviation (RMSD) difference in the coordinates of ligands. In this context, the SMPSO multi-objective swarm-intelligence algorithm has shown a remarkable performance. SMPSO is characterized by having an external archive used to store the non-dominated solutions and also as the basis of the leader selection strategy. In this paper, we analyze several SMPSO variants based on different archiving strategies in the scope of a benchmark of molecular docking instances. Our study reveals that the SMPSOhv, which uses an hypervolume contribution based archive, shows the overall best performance.

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Section: Methodsmentioning

confidence: 99%

A Study of Archiving Strategies in Multi-objective PSO for Molecular Docking

García-Nieto

López-Camacho

García-Godoy

et al. 2016

Lecture Notes in Computer Science

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“…According to the data shown, the lowest RMSD scores were obtained by the DE algorithm in comparison with the LGA. In fact, taking into account the classification of the inhibitor size shown in Table 2, the DE algorithm has solutions with lower RMSD values than the LGA ones for large ligands (11), small ligands (14), medium ligands (12) and urea cyclic ligands (7). To the contrary, the results obtained from the LGA algorithm were lower for large ligands (7), small ligands (9), medium ligands (10) and urea cyclic ligands (6).…”

Section: Biological Analysismentioning

confidence: 96%

“…As AutoDock and jMetal are written in different programming languages, to obtain an efficient implementation we have opted to develop a C++ version of jMetal. The result is a software package that integrates both tools [11], in such a way that the algorithms available in jMetal can be used to optimize the binding energy function of AutoDock 4.2. Consequently, AutoDock users also benefit because they have the choice of using different optimization techniques beyond the ones already incorporated inside the software, and researchers in metaheuristics can use a real world problem, like molecular docking in their work.…”

Section: Introductionintroductionmentioning

confidence: 99%

Solving molecular flexible docking problems with metaheuristics: A comparative study

López-Camacho

García-Godoy

García-Nieto

et al. 2015

Applied Soft Computing

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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: the scoring function and the search method being used to find the lowest binding energy solution. In this context, AutoDock 4.2 is a popular C++ software package in the bioinformatics community providing both elements, including two genetic algorithms, one of them endowed with a local search strategy. This paper principally focuses on the search techniques for solving the docking problem. In using the AutoDock 4.2 scoring function, the approach in this study is twofold. On the one hand, a number of four metaheuristic techniques are analyzed within an extensive set of docking problems, looking for the best technique according to the quality of the binding energy solutions. These techniques are thoroughly evaluated and also compared with popular well-known docking algorithms in AutoDock 4.2. The metaheuristics selected are: generational and a steady-state Genetic Algorithm, Differential Evolution, and Particle Swarm Optimization. On the other hand, a C++ version of the jMetal optimization framework has been integrated inside AutoDock 4.2, so that all the algorithms included in jMetal are readily available to solve docking problems. The experiments reveal that Differential Evolution obtains the best overall results, even outperforming other existing algorithms specifically designed for molecular docking.

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“…If the search for frameworks is not limited to Python, other popular frameworks should be considered: PlatEMO [45] in Matlab, MOEA [20] and jMetal [15] in Java, jMetalCpp [31] and PaGMO [3] in C++. Of course this is not an exhaustive list and readers may search for other available options.…”

Section: Related Workmentioning

confidence: 99%

Pymoo: Multi-Objective Optimization in Python

2020

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A B S T R A C TPython has become the programming language of choice for research and industry projects related to data science, machine learning, and deep learning. Since optimization is an inherent part of these research fields, more optimization related frameworks have arisen in the past few years. Only a few of them support optimization of multiple conflicting objectives at a time, but do not provide comprehensive tools for a complete multi-objective optimization task. To address this issue, we have developed pymoo, a multi-objective optimization framework in Python. We provide a guide to getting started with our framework by demonstrating the implementation of an exemplary constrained multiobjective optimization scenario. Moreover, we give a high-level overview of the architecture of pymoo to show its capabilities followed by an explanation of each module and its corresponding sub-modules. The implementations in our framework are customizable and algorithms can be modified/extended by supplying custom operators. Moreover, a variety of single, multi and many-objective test problems are provided and gradients can be retrieved by automatic differentiation out of the box. Also, pymoo addresses practical needs, such as the parallelization of function evaluations, methods to visualize low and high-dimensional spaces, and tools for multi-criteria decision making. For more information about pymoo, readers are encouraged to visit: https://pymoo.org

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jMetalCpp: optimizing molecular docking problems with a C++ metaheuristic framework

Abstract: jMetalCpp software adapted to AutoDock is freely available as a C++ source code at http://khaos.uma.es/AutodockjMetal/.

Cited by 28 publications

References 13 publications

A Study of Archiving Strategies in Multi-objective PSO for Molecular Docking

A Study of Archiving Strategies in Multi-objective PSO for Molecular Docking

Solving molecular flexible docking problems with metaheuristics: A comparative study

Pymoo: Multi-Objective Optimization in Python

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