International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 03 | Mar-2016 www.irjet.net p-ISSN: 2395-0072 © 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 671 Metaheuristic Techniques for Conformational Search SIEW MOOI LIM 1 , MD. NASIR SULAIMAN 2 , NORWATI MUSTAPHA 3 , ABU BAKAR MD. SULTAN 4 FACULTY OF COMPUTER SCIENCE AND INFORMATION TECHNOLOGY, UNIVERSITI PUTRA MALAYSIA, MALAYSIA ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract The drawback in conformational search (CS) is in locating the most stable conformation of a molecule with the minimum potential energy based on a mathematical function. The number of local minima grows exponentially with molecular size and this makes it that more difficult to arrive at a solution. It had been confirmed that CS belongs to the category of NP-hard (non-deterministic polynomial time) problem. Such complexity requires an equally long amount of time to achieve resolution. This phenomenon is thus known as the 'combinatorial explosion'. Metaheuristic techniques have been constantly used in solving CS problems. These population-based probabilistic techniques explore conformational space by random perturbation of atomic Cartesian coordinates or the torsion angles of rotatable bonds. These methods focus on exploring a search space with maximum efficacy. With one or more solutions in the beginning, metaheuristic method follows with a more iterative approach to optimize the search in promising areas away from local solutions. This method is often employed in circumstances where the exact solution methods are unfeasible within a limited time frame. As such, this paper presents various past metaheuristics approaches that have been brought forth in regards to the problem of an effective exploration of the conformational states of molecular systems. Each metaheuristic method is accompanied by its advantages and disadvantages. The concepts of each approach will be explained and their respective applications are discussed. Key Words: Model building methods, distance geometry, smoothing methods, systematic search 1. INTRODUCTION Conformational search (CS) is a term familiar to those in the field of applied mathematics and computational chemistry. CS is mathematically represented as a continuous global optimization problem. In CS, the variables are the torsion angles or coordinates that are used to represent the conformation of the molecule (e.g. polypeptide chain). The objective function value is the potential energy function. By varying the values of the variables, the global minimum value of the objective function can be achieved; that is to locate the most stable conformation of a molecule with the minimum potential energy. Years of research have seen CS performing important and widely used molecular modeling applications which include flexible rings and macrocycles molecules [1], cyclic, acyclic, mixed single molecules and host-guest complexes [2], molecules under inhibited conditions, peptide and protein folding [3], simulations of protein-ligand docking [4, 5] and various drug design applications such as Quantitative Structure Activity Relationship (QSAR), virtual screening of virtual libraries and active analog approaches [6]. The exact methods are unable to solve the complexity of CS problems. Therefore, metaheuristic techniques [7] including genetic algorithm have become mandatory and has attracted considerable attention especially from evolutionary algorithm community. We have introduced a novel real coded genetic algorithm which is capable in solving two CS application problems i.e minimizing a molecular potential energy function and finding the most stable conformation of pseudoethane through a molecular model that involves a realistic energy function [8-12]. The focus of this paper has been pivoted on the five major categories of approaches including five common metaheuristic techniques that have been applied to CS problems. 2. Application of Metaheuristic Techniques to Conformational Search Problems Metaheuristic techniques include all stochastic algorithms with randomization and local search. Randomization allows a shift from the algorithms in a local search to the global scale. Therefore, almost all metaheuristic algorithms were designed to suit the global optimization. Metaheuristic algorithms consist of two major components namely exploration (diversification) and exploitation (intensification). GA, Tabu search and particle swarm optimization are reported to heavily study on maintaining a good balance between exploration and exploitation in preserving diversity [13, 14]. The function of exploration is such that it generates diverse solutions in order to explore the search space on the global scale. On the other hand, exploitation draws upon the local search region, in which the information of a current good solution in this region will be further