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Finding the orientation of a ligand (small molecule) with the lowest binding energy to the macromolecule (receptor) is a complex optimization problem, commonly called ligand–protein docking. This problem has been usually approached by minimizing a single objective that corresponds to the final free energy of binding. In this work, we propose a new multi-objective strategy focused on minimizing: (1) the root mean square deviation (RMSD) between the co-crystallized and predicted ligand atomic coordinates, and (2) the ligand–receptor intermolecular energy. This multi-objective strategy provides the molecular biologists with a range of solutions computing different RMSD scores and intermolecular energies. A set of representative multi-objective algorithms, namely NSGA-II, SMPSO, GDE3 and MOEA/D, have been evaluated in the scope of an extensive set of docking problems, which are featured by including HIV-proteases with flexible ARG8 side chains and their inhibitors. As use cases for biological validation, we have included a set of instances based on new retroviral inhibitors to HIV-proteases. The proposed multi-objective approach shows that the predictions of ligand's pose can be promising in cases in which studies in silico are necessary to test new candidate drugs (or analogue drugs) to a given therapeutic target. [ABSTRACT FROM AUTHOR]

The efficient calibration of hydrologic models allows experts to evaluate past events in river basins, as well as to describe new scenarios and predict possible future floodings. A difficulty in this context is the need to adjust a large number of parameters in the model to reduce prediction errors. In this work, we address this issue with two complementary contributions. First, we propose a new lumped rainfall-runoff hydrologic model—called Qom—which is featured by a limited set of continuous decision variables associated with soil moisture and direct runoff. Qom allows to separate and quantify the volume of losses and excesses of the rainwater falling in a hydrographic basin, while a Clark's model is used to determine output hydrograms. Second, we apply a multi-objective optimization approach to find accurate calibrations of the model in a systematic and automatic way. The idea is to formulate the process as a bi-objective optimization problem where the Nash-Sutcliffe Efficiency coefficient and percent bias have to be minimized, and to combine the results found by a set of metaheuristics used to solve it. For validation purposes, we apply our proposal in six hydrographic scenarios, comprising river basins located in Spain, USA, Brazil and Argentina. The proposed approach is shown to minimize prediction errors of simulated streamflows with regards to those observed in these real-world basins. [ABSTRACT FROM AUTHOR]

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, and, therefore, drug responses from lung cancer patients. In this context, candidate drugs are being tested and in silico studies are necessary to know how these mutations affect the ligand binding site. This problem can be tackled by using a multi-objective approach applied to the molecular docking problem. According to the literature, few studies are related to the application of multi-objective approaches by minimizing two or more objectives in drug discovery. In this study, we have used four algorithms (NSGA-II, GDE3, SMPSO and MOEA/D) to minimize two objectives: the ligand–receptor intermolecular energy and the RMSD score. We have prepared a set of instances that includes the wild-type EGFR kinase domain and the same receptor with somatic mutations, and then we assessed the performance of the algorithms by applying a quality indicator to evaluate the convergence and diversity of the reference fronts. The MOEA/D algorithm yields the best solutions to these docking problems. The obtained solutions were analyzed, showing promising results to predict candidate EGFR inhibitors by using this multi-objective approach. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. However, while real problems could have hundreds or even thousands of decision variables, current benchmarks are normally adopted with relatively few decision variables (normally from 10 to 30). Furthermore, performing a constant number of evaluations does not provide information about the effort required by an algorithm to get a satisfactory set of solutions; this information would also be of interest in real scenarios, where evaluating the functions defining the problem can be computationally expensive. In this paper, we study the effect of parameter scalability in a number of state-of-the-art multiobjective metaheuristics. We adopt a benchmark of parameter-wise scalable problems (the Zitzler-Deb-Thiele test suite) and analyze the behavior of eight multiobjective metaheuristics on these test problems when using a number of decision variables that range from 8 up to 2048. By using the hypervolume indicator as a stopping condition, we also analyze the computational effort required by each algorithm in order to reach the Pareto front. We conclude that the two analyzed algorithms based on particle swarm optimization and differential evolution yield the best overall results. [ABSTRACT FROM AUTHOR]

This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques. Building upon our experiences with the well-known jMetal framework, we have developed a new multi-objective optimization software platform aiming not only at replicating the former one in a different programming language, but also at taking advantage of the full feature set of Python, including its facilities for fast prototyping and the large amount of available libraries for data processing, data analysis, data visualization, and high-performance computing. As a result, jMetalPy provides an environment for solving multi-objective optimization problems focused not only on traditional metaheuristics, but also on techniques supporting preference articulation, constrained and dynamic problems, along with a rich set of features related to the automatic generation of statistical data from the results generated, as well as the real-time and interactive visualization of the Pareto front approximations produced by the algorithms. jMetalPy offers additionally support for parallel computing in multicore and cluster systems. We include some use cases to explore the main features of jMetalPy and to illustrate how to work with it. [ABSTRACT FROM AUTHOR]

• A semantic approach is proposed to inject problem domain knowledge in optimization. • A top-level ontology is developed to represent users preferences and constraints. • The semantic model is implemented and integrated with jMetal algorithmic library. • The proposal is validated on multi-objective optimization problems TSP and RND. • Domain experts are able to semantically annotate required knowledge in optimization. In the field of complex problem optimization with metaheuristics, semantics has been used for modeling different aspects, such as: problem characterization, parameters, decision-maker's preferences, or algorithms. However, there is a lack of approaches where ontologies are applied in a direct way into the optimization process, with the aim of enhancing it by allowing the systematic incorporation of additional domain knowledge. This is due to the high level of abstraction of ontologies, which makes them difficult to be mapped into the code implementing the problems and/or the specific operators of metaheuristics. In this paper, we present a strategy to inject domain knowledge (by reusing existing ontologies or creating a new one) into a problem implementation that will be optimized using a metaheuristic. Thus, this approach based on accepted ontologies enables building and exploiting complex computing systems in optimization problems. We describe a methodology to automatically induce user choices (taken from the ontology) into the problem implementations provided by the jMetal optimization framework. With the aim of illustrating our proposal, we focus on the urban domain. Concretely, we start from defining an ontology representing the domain semantics for a city (e.g., building, bridges, point of interest, routes, etc.) that allows defining a-priori preferences by a decision maker in a standard, reusable, and formal (logic-based) way. We validate our proposal with several instances of two use cases, consisting in bi-objective formulations of the Traveling Salesman Problem (TSP) and the Radio Network Design problem (RND), both in the context of an urban scenario. The results of the experiments conducted show how the semantic specification of domain constraints are effectively mapped into feasible solutions of the tackled TSP and RND scenarios. This proposal aims at representing a step forward towards the automatic modeling and adaptation of optimization problems guided by semantics, where the annotation of a human expert can be now considered during the optimization process. [ABSTRACT FROM AUTHOR]