PREDICTIVE QSAR MODELING

描述（由申请人提供）：高效和准确的虚拟筛选方法的发展仍然是计算药物发现领域的一个巨大挑战。本竞争性修订申请是为了响应标题为no - od -09-058的申请而提交的：NIH宣布竞争性修订申请的恢复法案资金的可用性。本申请的母基金主要用于增强预测QSAR建模技术及其在大型化学数据库中识别计算命中值的应用。在目前资助的项目的研究过程中，我们已经意识到，一些QSAR建模方法实际上可以扩展到基于结构的药物发现的互补领域。基于我们在化学信息学和QSAR建模方面的经验，本提案旨在开发新的计算效率高的化学信息学方法，应用于基于结构的虚拟筛选和配体姿态评分。此外，我们与基于结构建模方法的专家N. Dokholyan博士合作，努力将这些化学信息学启发的方法与基于经验力场的方法结合起来，从目前可用于实验生物筛选的超大化学文库中选择越来越小的靶向高亲和力配体亚群。我们的混合方法的最终目标是在受体结合构象中获得一小组高亲和力的计算命中，这些命中可以通过实验验证。这一目标将通过围绕以下三个具体目标构建拟议的研究来实现：1)开发基于新型高效结构的化学信息学虚拟筛选方法；2)利用新型配体-受体复合物的几何化学描述符开发基于统计结构的位姿评分和结合函数；3)开发结合统计和经验评分函数的混合方法，用于姿态优化和绑定亲和力预测。我们期望本应用程序中先进方法的实施将显著提高计算机辅助药物发现的先进计算工具的数量、效率和可用性。

DESCRIPTION (provided by applicant): The development of highly efficient and accurate approaches to virtual screening continues to represent a formidable challenge in the field of computational drug discovery. This Competitive Revision application is submitted in response to the NOT-OD-09-058 titled: NIH Announces the Availability of Recovery Act Funds for Competitive Revision Applications. The parent grant of this application is focused on enabling significant enhancements in predictive QSAR modeling technologies and their application to identifying computational hits in large chemical databases. In the course of studies enabled by the currently funded project we have realized that some of the QSAR modeling approaches could be actually extended towards a complimentary filed of structure based drug discovery. Building upon our experience in cheminformatics and QSAR modeling, this proposal aims to develop novel computationally efficient cheminformatics approaches applied to structure based virtual screening and ligand pose scorings. Furthermore, in collaboration with a specialist in structure based modeling approaches, Dr. N. Dokholyan, we endeavor to combine these cheminformatics-inspired methodologies with empirical forcefield based approaches towards the selection of progressively smaller subsets of target-specific high-affinity ligands from exceedingly large chemical libraries available today for experimental biological screening. The ultimate goal of our hybrid methodology is to arrive at a small set of high-affinity computational hits in receptor-bound conformations that could be validated experimentally. This goal will be achieved by structuring the proposed studies around the following three Specific Aims: 1) Develop novel highly efficient structure based cheminformatics approaches to virtual screening; 2) Develop statistical structure based pose scoring and binding functions using novel geometrical chemical descriptors of ligand-receptor complexes; 3) Develop hybrid approaches integrating statistical and empirical scoring functions for pose refinement and binding affinity prediction. We expect that the implementation of the methods advanced in this application will significantly enhance the repertoire, efficiency, and availability of advanced computational tools for computer aided drug discovery. PUBLIC HEALTH RELEVANCE: Computer-aided drug discovery methods play significant role in facilitating the selection of most viable lead compounds for subsequent pharmaceutical development. It is critical to develop most computationally efficient and theoretically robust approaches to ensure the usefulness of computational predictions of hit and lead chemical structures. This proposal advances the efficient and robust computational workflow for structure based virtual screening of available compound collections to arrive at a small number of reliable and experimentally testable candidate molecules with high predicted binding affinity to their biological targets.