Deployment of Quantum Mechanical Pairwise Energy Decomposition for Drug Discovery

描述（由申请人提供）：计算机药物发现的当前技术状态几乎完全依赖于分子力学力场，如AMBER和经验势。众所周知，虽然这些方法对于某些应用是优秀的，但迄今为止，它们被证明对于彻底理解酶抑制剂系统的相互作用不那么令人满意。为了解决这些问题，我们的线性缩放，量子力学（QM）算法将应用于第一阶段的努力，以进一步研究，转移和验证基于QM的工具，以获得一组目标和大量抑制剂之间的成对能量分解（PWD）。此外，将使用Discovery Machine（DM）平台充分探索此过程的多方面工作流程，以便为继续开发奠定基础，并开始解决此理论水平的易用性问题。在第二阶段的工作中，该PWD技术与我们专有的QMScore技术一起沿着将作为InteractionProfiler工具开发，并通过利用新的行业合作来验证许多结构。我们还将开发必要的客户端/服务器软件和数据库后端，以便在工业或政府环境中正确利用这些强大的QM工具。DM将继续在这一过程中发挥重要作用，这一快速SBIR的最终目标将是一个智能和自适应的系统，用于基于QM的药物发现，能够扩大用户对酶-抑制剂相互作用的类型和强度的理解，这些相互作用在用户的计算机药物发现工作中发挥重要作用。

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.