Recommended Search Algorithms

Since computation time is the most important bottleneck to conformation searching, the following list starts with the methods most amenable to the largest molecular systems  

Sources, which compare conformation search algorithms are 

Jensen, Introduction to Computational Chemistry John Wiley Sons, New York (1999). 

Leach Molecular Modelling Principles and Applications Longman, Essex (1996). M. Vasquez, G. Nemethy, fJ. A. Scheraga, Cltem. Rev. 94, 2183 (1994). 

Scheraga, Computer Simulation of Biomolecular Systems Voume 2 W. F. v. Gunsteren, P. K. Weiner, A. J. Wilkinson, Eds., ESCOM, Leiden (1993). 

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Scales (1986) recommends the Polak Ribiere version because it has slightly better convergence properties. Scales also gives an algorithm which is used for both methods that differ only in the formula for the updating of the search vector. 

Therefore, the shelf life is the root smaller than 28.90. A simple and practical tool to compute the roots of Equation (12) is perhaps solving the following equivalent problem. Find such that it minimizes the absolute value of /( ). This root is obtained by using the quasi-Newton line search (QNLS) algorithm [13]. The computer program requires an initial point and we recommend using the value... 

There are two basic types of unconstrained optimization algorithms (1) those requiring function derivatives and (2) those that do not. Here we give only an overview and refer the reader to Sec. 3 or the references for more details. The nonderivative methods are of interest in optimization applications because these methods can be readily adapted to the case in which experiments are carried out directly on the process. In such cases, an actual process measurement (such as yield) can be the objective function, and no mathematical model for the process is required. Methods that do not require derivatives are called direct methods and include sequential simplex (Nelder-Meade) and Powells method. The sequential simplex method is quite satisfactory for optimization with two or three independent variables, is simple to understand, and is fairly easy to execute. Powell s method is more efficient than the simplex method and is based on the concept of conjugate search directions. This class of methods can be used in special cases but is not recommended for optimization involving more than 6 to 10 variables. 

In general, if it is feasible to carry out an exhaustive search, then that is to be recommended. As the sequential-replacement algorithm is fairly fast, it can always be used first to provide an indication of the maximum size of the subset that is likely... 

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