A New Multi-Objective Approach for Molecular Docking Based on RMSD and Binding Energy

Ligand-protein docking is an optimization problem based on predicting the position of a ligand with the lowest binding energy in the active site of the receptor. Molecular docking problems are traditionally tackled with single-objective, as well as with multi-objective approaches, to minimize the binding energy. In this paper, we propose a novel multi-objective formulation that considers: the Root Mean Square Deviation (RMSD) difference in the coordinates of ligands and the binding (intermolecular) energy, as two objectives to evaluate the quality of the ligand-protein interactions. To determine the kind of Pareto front approximations that can be obtained, we have selected a set of representative multi-objective algorithms such as NSGA-II, SMPSO, GDE3, and MOEA/D. Their performances have been assessed by applying two main quality indicators intended to measure convergence and diversity of the fronts. In addition, a comparison with LGA, a reference single-objective evolutionary algorithm for molecular docking (AutoDock) is carried out. In general, SMPSO shows the best overall results in terms of energy and RMSD (value lower than 2A for successful docking results). This new multi-objective approach shows an improvement over the ligand-protein docking predictions that could be promising in in silico docking studies to select new anticancer compounds for therapeutic targets that are multidrug resistant.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

On the automatic design of multi‑objective particle swarm optimizers: experimentation and analysis.

Research in multi-objective particle swarm optimizers (MOPSOs) progresses by proposing one new MOPSO at a time. In spite of the commonalities among different MOPSOs, it is often unclear which algorithmic components are crucial for explaining the performance of a particular MOPSO design. Moreover, it is expected that different designs may perform best on different problem families and identifying a best overall MOPSO is a challenging task. We tackle this challenge here by: (1) proposing AutoMOPSO, a flexible algorithmic template for designing MOPSOs with a design space that can instantiate thousands of potential MOPSOs; and (2) searching for good-performing MOPSO designs given a family of training problems by means of an automatic configuration tool (irace). We apply this automatic design methodology to generate a MOPSO that significantly outperforms two state-of-the-art MOPSOs on four well-known bi-objective problem families. We also identify the key design choices and parameters of the winning MOPSO by means of ablation. FAutoMOPSO is publicly available as part of the jMetal framework.Funding for open access charge: Universidad de Málaga / CBU

Docking Inter/Intra-Molecular Mediante Metaheurísticas Multi-objetivo

El Acoplamiento Molecular (Molecular Docking) es un problema de optimización de gran complejidad que consiste en predecir la orientación de dos moléculas: el ligando y el receptor, de manera que formen un complejo molecular energéticamente estable. El docking molecular es un problema tradicionalmente tratado con éxito mediante metaheurísticas para la optimización de un objetivo: la mínima energía libre de unión. Sin embargo, en la literatura actual no se encuentran muchos trabajos que traten este problema desde el punto de vista multiobjetivo. En este sentido, todavía no existen estudios comparativos con el fin de dilucidar qué técnica (o qué tipo de ellas) ofrece un mejor rendimiento en general. En este estudio realizamos una comparativa experimental de una serie de algoritmos multiobjetivo representativos del estado del arte actual, para la resolución de instancias complejas de docking molecular. En concreto, los algoritmos evaluados son: NSGA-II, ssNSGA-II, SMPSO, GDE3, MOEA/D y SMS-EMOA. Para ello, hemos seguido un enfoque de optimización basado en las energías inter- e intra-molecular, siendo éstos los dos objetivos a minimizar. En la evaluación de los al- goritmos hemos aplicado métricas de rendimiento para medir la convergencia y la diversidad de los frentes de Pareto resultantes, respecto a frentes de referencia calculados. Además, en comparación con soluciones mono-objetivo obtenidas por técnicas de referencia en el problema (LGA), comprobamos cómo los algoritmos multi-objetivo evaluados son capaces de obtener conformaciones moleculares de mínima energía de acoplamiento.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Estudio de Estrategias de Archivo en PSO Multi-Objetivo para el Docking Molecular

El acoplamiento molecular es un problema de optimización complejo cuyo objetivo es la predicción de la posición de un ligando en el sitio activo de un receptor con la mínima energía de unión. Este problema puede ser formulado como un problema de optimización de dos objetivos que minimiza la energía de unión y la desviación de la media cuadrática de las posiciones atómicas (RMSD) de los ligandos. En este contexto, el algoritmo multi-objetivo de swarm-intelligence SMPSO mostró un rendimiento destacable. SMPSO se caracteriza por usar un archivo externo para almacenar las soluciones no dominadas y como base para estrategia de selección de líder. En este artículo, se analizan diferentes variantes de SMPSO basadas en diferentes estrategias de archivo utilizando un benchmark de instancias moleculares. Este estudio revela que la variante SMPSOhv obtiene los mejores resultados.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A Study About Meta-Optimizing the NSGA-II Multi-Objective Evolutionary Algorithm.

The automatic design of multi-objective metaheuristics is an active research line aimed at, given a set of problems used as training set, to find the configuration of a multi-objective optimizer able of solving them efficiently. The expected outcome is that the auto-configured algorithm can be used of find accurate Pareto front approximations for other problems. In this paper, we conduct a study on the meta-optimization of the wellknown NSGA-II algorithm, i.e., we intend to use NSGA-II as an automatic configuration tool to find configurations of NSGA-II. This search can be formulated as a multi-objective problem where the decision variables are the NSGA-II components and parameters and the the objectives are quality indicators that have to be minimized. To develop this study, we rely on the jMetal framework. The analysis we propose is aimed at answering the following research questions: RQ1 - how complex is to build the meta-optimization package?, and RQ2 - can accurate configurations be found? We conduct an experimentation to give an answer to these questions.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Evolver: Meta-optimizing multi-objective metaheuristics.

Evolver is a tool based on the formulation of the automatic configuration and design of multi-objective metaheuristics as a multi-objective optimization problem that can be solved by using the same kind of algorithms; i.e., we are applying a meta-optimization approach. Evolver provides highly configurable implementations of representative multi-objective solvers which can be automatically configured from a number of multi-objective problems used as the training set and a list of quality indicators which are the objectives to be optimized. Our tool is based on the jMetal framework, so a large number of existing algorithms can be used as meta-optimizers. A graphical user interface allows scientists to easily define auto-configuration scenarios, thus simplifying the complex process of finding high-quality algorithm settings.Partial funding for open access: Universidad de Málaga / CBU