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STATE COLLEGE, PA, United States

Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 769.21K | Year: 2008

DESCRIPTION (provided by applicant): The current state of the art of in silico drug discovery relies almost exclusively on molecular mechanics force fields, such as AMBER, 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. To address these issues, our linear scaling, quantum mechanics (QM) algorithm will be applied in the Phase I effort to further research, transfer, and validate a QM-based tool to derive the pairwise energy decomposition (PWD) between a set of targets and a large population of inhibitors. Further, the multifaceted workflow of this process will be fully explored with the Discovery Machine (DM) platform in order to set the groundwork for continued development and to begin to address the ease of use concerns with this level of theory. In the Phase II effort, this PWD technology, along with our proprietary QMScore technology, will be developed as an InteractionProfiler tool and validated against a number of structures by leveraging new industry collaborations. We will also develop the client/server software and database backend necessary to properly exploit these powerful QM tools in an industrial or a government setting. DM will continue to play a significant roll in this process, and the ultimate goal of this fast track SBIR will be an intelligent and adaptive system for QM-based drug discovery with the capability of expanding the user's understanding of the types and strengths of enzyme-inhibitor interactions that play an important roll in the user's in silico drug discovery efforts.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 108.74K | Year: 2008

DESCRIPTION (provided by applicant): Combined Improving human health by enabling the development of drugs faster and cheaper is an important part of the NIH mission. This is partially achieved by introducing and constantly improving enabling technologies. One such technology is structure based drug design. Determining the structure of a small molecule (drug candidate or lead compound) to a biological receptor (protein implicated in disease) is a necessary step in this methodology. The dominant experimental approach used to achieve this goal is X-ray crystallography, while nuclear magnetic resonance (NMR) plays a lesser role. X-ray techniques provide astounding insights into the structure of protein-ligand complexes, but can be hampered by the resolution to w hich a crystal diffracts and the refinement process can be hampered by the lack of good potentials for novel small molecule compounds. The aim of the proposed research is to extend and further validate our linear-scaling semiempirical quantum mechanical mo lecular mechanical X-ray refinement approach (QM/MMXray). In general the limits of applicability will be researched and in particular the following question will be posed in the Phase I project: Can QM/MMX-ray provide better structure quality for a protein -ligand complex as measured by various crystallographic metrics (R, Rfree, ?A-weighted Fourier difference maps, etc.)? Upon successful completion of the Phase I project we will further enhance and extend the QM/MMXray method and produce commercial quality code. If this approach proves robust enough it is anticipated that the use of QM/MMXray in structure-based design efforts will be enhanced and the Xray tool and service market size can be further expanded. Significantly, the tool-box of structure based dru g design will gain an important new method which will enable drug development for targets inaccessible to today's mainstream drug discovery paradigm. Thus, in the near future important underserved diseases can be targeted more efficiently. PUBLIC HE ALTH RELEVANCE: The successful completion of the Fast-Track STTR grant will have a major impact on improving human health. It will improve the quality of protein structures, facilitate the understanding of biomolecular dynamics and will provide higher qual ity structural insights into protein/ligand (drug) interactions which will enhance our ability to rationally design novel therapeutics for human diseases.


Grant
Agency: Department of Health and Human Services | Branch: | Program: STTR | Phase: Phase II | Award Amount: 433.80K | Year: 2007

Not avaiable.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 98.91K | Year: 2007

DESCRIPTION (provided by applicant): The current state of the art of in silico drug discovery relies almost exclusively on molecular mechanics force fields, such as AMBER, 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. To address these issues, our linear scaling, quantum mechanics (QM) algorithm will be applied in the Phase I effort to further research, transfer, and validate a QM-based tool to derive the pairwise energy decomposition (PWD) between a set of targets and a large population of inhibitors. Further, the multifaceted workflow of this process will be fully explored with the Discovery Machine (DM) platform in order to set the groundwork for continued development and to begin to address the ease of use concerns with this level of theory. In the Phase II effort, this PWD technology, along with our proprietary QMScore te chnology, will be developed as an InteractionProfiler tool and validated against a number of structures by leveraging new industry collaborations. We will also develop the client/server software and database backend necessary to properly exploit these powe rful QM tools in an industrial or a government setting. DM will continue to play a significant roll in this process, and the ultimate goal of this fast track SBIR will be an intelligent and adaptive system for QM-based drug discovery with the capability of expanding the user's understanding of the types and strengths of enzyme-inhibitor interactions that play an important roll in the user's in silico drug discovery efforts.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 147.91K | Year: 2011

DESCRIPTION (provided by applicant): Improving human health by enabling the development of drugs faster and cheaper is an important part of the NIH mission. This is partially achieved by introducing and constantly improving enabling technologies. One suchtechnology is structure based drug design. Determining the structure of a small molecule (drug candidate or lead compound) to a biological receptor (protein implicated in disease) is a necessary step in this methodology. The dominant experimental approachused to achieve this goal is X- ray crystallography, while nuclear magnetic resonance (NMR) plays a lesser role in spite of large investments both in academia and industry. NMR is hampered by the size of protein that can be studied and the need to go through a lengthy structure determination process. However, with the advent of fragment based drug design, NMR is playing a much larger role and it could play an even greater role if it was possible to reduce the time effort necessary to solve the structure ofa protein-ligand complex. Moreover, in cases where it is not possible to obtain a crystal NMR can play a significant role. Through the use of solid-state NMR studies membrane proteins or proteins with solubility problems can be studied or in cases where only homology models of a protein are available NMR could play a role through the validation of active site structure hypotheses generated in homology modeling studies. The aim of the proposed research is to extend and commercialize QuantumBio's successful linear-scaling semiempirical quantum mechanical NMR approach (NMRScore) to chemical shift perturbation (CSP) analysis through the addition of target-observed CSP and ab initio NMR methods. In Phase I of this proposal the limits of applicability will be explored. In the Phase II proposal extension of the methodology via reparameterization of 1H, 13C 17O and 15N NMR will be carried out and a new classical NMR predictor will be developed. Furthermore, the streamlining of the workflow will be researched and implemented. Finally, this proposal is aiming to fully productize and commercialize this breakthrough technology. It is anticipated that by making this application commercially available the use of NMR in structure-based design efforts will be enhanced and theNMR tool and service market size can be further expanded. Significantly, the tool-box of structure based drug design will gain an important new method which will enable drug development for targets inaccessible to today's mainstream drug discovery paradigm. Thus, in the near future important underserved diseases can be targeted more efficiently. PUBLIC HEALTH RELEVANCE: The successful completion of the Fast-Track SBIR grant will have a major impact on improving human health. It will improve the quality of protein structures, facilitate the understanding of biomolecular dynamics and will provide higher quality structural insights into protein/ligand (drug) interactions which will enhance our ability to rationally design novel therapeutics for human diseases.

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