Into molecular machines

Open source tools are not static and sometimes it is necessary to go back to the beginning and relearn what you already know with the new features. PyMol is a useful tool ( link to PyMol wiki ) that has evolved and what I wanted to do was revise and extend it to use the structure of the atom and nucleus to produce a predictive chemical system that calculated the tree path from the elements to complex structure and systems. It involves using newly developed techniques with tree analysis similar to the backpack solution. Carbon is perhaps the only substrate molecule for life or complex systems due to its even and flexible tetrahedral bonding. I personally doubt that any complex life could occur by chance evolution with atomic cores other than carbon. I almost shortened it to the chemical symbol C and realized that that operator symbol is overloaded too many times in my discussions to represent c-speed of light in vacuum, C=the computer language, and sometimes as a set of convenient variables like a,b,c. So for clarity I must say Carbon.

It can be combined with other tools like my personal wiki, blender, OpenGl, SDL, matplotlib, SciPy, graphing trees, and many other utilities that also have Python interfaces. By computing the molecular interface at the electron and nuclear level, it is possible to predict what process will ensue in a particular medium. I am sure there are a vast number of pathways through the maze of chemical interaction and just the factorial combination of carbon containing molecules with single double and triple bonds branches to infinity very quickly. Another thing that makes carbon unique is the fact that it forms double bonds that create chirality, besides its ability to encompass all of three space in its bond structures as well as the ease with which it forms resonant rings.

A large number of databases exist with chemical information and the analysis and prediction of new structure can be combined with informational sources to determine if the underlying principles do predict the structure and function of a specific catalyzed chemical compound.