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Abstract:
The current state of the art of in silico drug discovery or computer aided drug discovery relies almost exclusively on molecular mechanics force fields, such as AMBER and CHARMM, and empirical potentials. It is well known that while these approaches are excellent for certain applications, they have thus far proven less then satisfactory for thorough understanding the interactions of enzyme inhibitor systems when used in Ki or IC5O prediction and best pose selection. To address this issue, in the first part of the proposed research, we will utilize our linear scaling, quantum mechanics algorithm and collaborate with our industrial partners to further research, develop, and validates quantum mechanics-based score function, called QMScore, capable of predicting Ki and binding modes to the levels of accuracy required by the in silico drug discovery world. In the second part of the project, we utilize a knowledge management and artificial intelligence platform to aide in the usability of this advanced methodology by researching the relationships between structure and QM convergence-the ultimate goal being the development of an intelligent and adaptive system for drug discovery.
| Limitations: |
 APPROVED FOR PUBLIC RELEASE |
| Description: |
Final rept. |
| Pages: |
20 |
| Report Date: |
03 FEB 2006 |
| Contract Number: |
W911NF05C0063 |
| Report Number: |
A570744 |
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Keywords relating to this report:
