PREDICTIVE QSAR MODELING

描述（由申请人提供）：开发高效和准确的虚拟筛选方法仍然是计算药物发现领域的一项艰巨挑战。本竞争性修订申请是为了响应标题为“NIH宣布竞争性修订申请的恢复法案资金可用性”的NOT-OD-09-058而提交的。该申请的母基金主要用于显著增强预测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一个免费的基于结构的药物发现领域。基于我们在化学信息学和QSAR建模方面的经验，该建议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，在与专家的合作，在基于结构的建模方法，N。Dokholyan，我们奋进结合联合收割机这些化学信息学启发的方法与经验力场为基础的方法，逐步较小的子集的目标特异性高亲和力配体的选择，从非常大的化学库今天可用于实验生物筛选。我们的混合方法的最终目标是达到一个小的一套高亲和力的计算命中受体结合的构象，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）开发新的高效的基于结构的化学信息学方法来进行虚拟筛选; 2）使用配体-受体复合物的新的几何化学描述符开发基于统计结构的姿势评分和结合功能; 3）开发整合统计和经验评分函数的混合方法，用于姿态细化和结合亲和力预测。我们预计，在本申请中先进的方法的实施将显着提高剧目，效率和先进的计算机辅助药物发现的计算工具的可用性。 公共卫生相关性：计算机辅助药物发现方法在为后续药物开发选择最可行的先导化合物方面发挥着重要作用。这是至关重要的，以开发最有效的计算和理论上强大的方法，以确保命中和铅化学结构的计算预测的有用性。该提议推进了用于基于结构的可用化合物集合的虚拟筛选的高效且稳健的计算工作流程，以获得少量可靠且实验上可测试的候选分子，其具有对其生物靶标的高预测结合亲和力。

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.