We present results from the application of two conformational searching methods: genetic algorithms (GA) and direct search methods for finding low energy conformations of organic molecules. GAS are in a class of biologically motivated optimization methods that evolve a population of individuals in w
Conformational searching methods for small molecules. II. Genetic algorithm approach
โ Scribed by R.S. Judson; E.P. Jaeger; A.M. Treasurywala; M.L. Peterson
- Publisher
- John Wiley and Sons
- Year
- 1993
- Tongue
- English
- Weight
- 913 KB
- Volume
- 14
- Category
- Article
- ISSN
- 0192-8651
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โฆ Synopsis
We demonstrate the use of a genetic algorithm (GA) search procedure for finding low-energy conformations of small to medium organic molecules (1-12 rotatable bonds). GAS are in a class of biologically motivated optimization methods that evolve a population of individuals where individuals who are more "fit" have a higher probability of surviving into subsequent generations. Here, an individual is a conformation of a given molecule and the fitness is the molecule's conformational energy. In the course of a simulated evolution, the population produces conformations having increasingly lower energy. We test the GA method on a suite of 72 molecules and compare the performance against the CSEARCH algorithm in Sybyl. For molecules with more than eight rotatable bonds, the GA method is more efficient computationally and as the number of rotatable bonds increases the relative efficiency of the GA method grows. The GA method also found energies equal to or lower than the energy of the relaxed crystal structure in the large majority of cases.
๐ SIMILAR VOLUMES
In a continuing effort to provide the computational community with a reference work comparing all of the available conformer searching methods, we have exposed the standard set of small molecules to two commonly used stochastic searching techniques. Advantages and limitations of these methods are di
A genetic algorithm-driven search method GAP1.0; Genetic . Algorithm Peptide search, version 1.0 has been developed for the computational exploration of peptide conformational space. The suitability of a variety of genetic algorithm operators was evaluated through representative calculations w x ลฝ .