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Automatic molecular design using evolutionary techniques

Al Globus,*, a, John Lawtonb, and Todd Wipkeb

aMRJ Technology Solutions, Inc. at NASA Ames Research Center
bUniversity of California at Santa Cruz

This is an abstract for a presentation given at the
Sixth Foresight Conference on Molecular Nanotechnology.
The full article is available in preliminary form at http://www.foresight.org/Conferences/MNT6/Papers/Globus/index.html.

 

Molecular nanotechnology is the precise, three-dimensional control of materials and devices at the atomic scale. An important part of nanotechnology is the design of molecules for specific purposes. This paper describes early results using genetic software techniques to automatically design molecules under the control of a fitness function. The fitness function must be capable of determining which of two arbitrary molecules is better for a specific task. The software begins by generating a population of random molecules. The population is then evolved towards greater fitness by randomly combining parts of the better individuals to create new molecules. These new molecules then replace some of the worst molecules in the population. The unique aspect of our approach is that we apply genetic crossover to molecules represented by graphs, i.e., sets of atoms and the bonds that connect them. We present evidence suggesting that crossover alone, operating on graphs, can evolve any possible molecule given an appropriate fitness function and a population containing both rings and chains. Prior work evolved strings or trees that were subsequently processed to generate molecular graphs. In principle, genetic graph software should be able to evolve other graph representable systems such as circuits, transportation networks, metabolic pathways, computer networks, etc.


*Corresponding Address:
Al Globus
MRJ Technology Solutions
email: globus@nas.nasa.gov; Web: http://science.nas.nasa.gov/~globus/home.html



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