Using adaptive mutation to accelerate the convergence of immune algorithms for prediction of 3D molecular structure

Abstract

The prediction of the lowest energy 3D structure of a molecule is related to the global minimum of the molecular potential energy function. The search for the global minimum of this function is very difficult since the number of its local minimizers grows exponentially with the molecule size. In this paper, we adopt an efficient adaptive mutation approach to provide a solution to the problem of finding the optimal value of the mutation rate, p_m, in order to fast the convergence of immune algorithms for minimizing the molecular potential energy function. This approach decreases the value of p_m for high-fitness solutions to sustain the convergence capacity of the immune algorithms and increases the value of p_m for low-fitness solutions to maintain diversity in the population. Computational results for problems with up to 300° of freedom are presented and are favorably compared with other existing methods from the literature. Also the results indicate that the proposed method is promising as it produces high-quality solutions with low computational costs.

Keywords:

• 3D structure prediction
• immune algorithms
• adaptive mutation
• energy function