An in silico design algorithm has been developed to design binding ligands for protein targets of known three-dimensional structure. In this method, the binding energy of a candidate ligand is used to ascribe it a probability of binding. A sample of a virtual library of candidate ligands is then used to ascribe implicit weights to all the ligands in the library. These weights are used to obtain virtual sub-libraries which collectively carry a greater probability to bind to the target. This algorithm is presented along with validation studies on the different algorithmic components, demonstrating how optimization of the design method can be best achieved.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/41067 |
| Date | 19 May 2010 |
| Creators | Enekwa, C. Denise |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Thesis |
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