On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Fast Global Optimization of Difficult Lennard-Jones Clusters
Computational Optimization and Applications
Equivalent formulations and necessary optimality conditions for the Lennard–Jones problem
Journal of Global Optimization
Efficient Algorithms for Large Scale Global Optimization: Lennard-Jones Clusters
Computational Optimization and Applications
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The problem of molecular docking is defined as that of finding a minimum energy configuration of a pair of molecular structures (usually consisting of proteins, DNA or RNA fragments). It is often assumed that the two interacting structures can be considered as rigid bodies and that it is of interest to researchers to develop methods which enable to discover the potential binding sites. Many different models have been proposed in the literature for the definition of the potential energy between two molecular structures, most of which contain at least a term (known as Van Der Waals interaction) which accounts for pairwise attraction between atoms, a repulsion term and a term which takes into account electrostatic forces (Coulomb interaction). Some well known models, and in particular those used in rigid docking, are based on the assumption that the only terms which are relevant in the process of docking are pairwise interactions between atoms belonging to the two different parts of the structure. In this paper the problem of finding the lowest energy configuration of a pair of biomolecular structures, considered as rigid bodies, is defined and formulated as a global optimization problem. In terms of dimension of the search space this formulation is not 'high-dimensional', as there are only six degrees of freedom: 3 translation and 3 rotation parameters. However the energy surface of the docking problem is characterized by a huge number of local minima; moreover each function evaluation is quite expensive (interesting structures usually possess a few thousand atoms each). So there is a strong need both of local and of global optimization procedures. In this paper a local optimization technique, based upon standard non linear programming software and a penalized objective function, is introduced and its potential usefulness in the context of global optimization is outlined.