Metropolis Monte Carlo procedure

However, it is normally assumed that the conformers that bind to target sites will be those with a minimum potential energy. Since molecules may have large numbers of such metastable conformers a number of techniques, such as the Metropolis Monte Carlo method and comparative molecular field analysis (CoMFA), have been developed to determine the effect of conformational changes on the effectiveness of docking procedures. 

To overcome the inefficiency of Metropolis Monte Carlo, which searches all of the conformational space but does so very slowly, a procedure to move rapidly through the space of local minima has been devised.177 The energy of a random starting conformation is minimized. Then, a random change (selected in the range 0 to 2ir) is made in a randomly chosen dihedral angle, and the... 

Another procedure to overcome the inefficiency of Metropolis Monte Carlo is adaptive importance sampling.194-196 In this technique, the partition function (and quantities derived from it, such as the probability of a given conformation) is evaluated by continually upgrading the distribution function (ultimately to the Boltzmann distribution) to concentrate the sampling in the region (s) where the probabilities are highest. 

The well-known Metropolis Monte Carlo (MMC) procedure randomly samples conformational space according to the Boltzmann distribution of (distinguishable) conformations [34] ... 

We overcome this problem by using Metropolis Monte Carlo (MMC) sampling [42] within each macrostate. In contrast with Monte Carlo integration, which uses a fixed kernel to select each sample point Q(i), in MMC a trial sample conformation Q(iy is selected with a transition distribution centered about the previous accepted sample conformation 2, 1). We accept CI(,) with probability pMMC, which is determined as in standard MMC procedure except that the potential is modified to account for the restriction of sampling to the macrostate region defined by w ... 

Monte Carlo methods are perhaps the most frequently used in computational statistical mechanics. In particular, the Metropolis Monte Carlo technique has been used extensively in simulation of liquids. Monte Carlo methods are probabilistic, rather than deterministic, procedures atoms are moved more or less randomly during the course of the simulation. In a Metropolis Monte Carlo simulation of a molecular system, the following steps would be followed ... 

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