Predictive QSAR Modeling

描述（由申请人提供）：本项目的主要目标是开发、验证和提供高效的计算工具，用于快速可靠地预测药物样分子的生物活性和/或相关药物特性。我们计划开发具有统计学显著性和稳健性的定量构效关系（QSAR）方法，该方法结合了严格的验证程序，并导致模型具有较高的预测能力和实用性。这些方法建立在相似性原则之上，即，化学结构的相似性或多样性决定了它们生物作用的相似性或多样性。我们认为，化学相似性应在目标属性的背景下进行评估，并采用客观的相似性和多样性函数，以实现在描述符空间中的化合物的生物学意义的聚类。因此，我们的方法采用变量选择程序，旨在确定相对于目标属性最相关的描述符。最重要的是，我们的方法是严格的模型验证与外部数据集，以确保最高的命中率时，预测QSAR模型最终应用于筛选化学数据库或虚拟库的生物活性化合物。本提案中与其具体目标相对应的四个主要重点领域包括：开发新的，主要是非线性QSAR方法，如k近邻（kNN）和支持向量机（SVM）方法。重点将是开发化学结构的新描述符，以及基础方法的效率、自动化和统计稳健性，以将其应用于大型商业数据集。.开发有效和客观的QSAR模型验证方法，确定模型适用范围，并最大限度地提高模型的预测能力。这些研究应导致建立广泛接受的可靠和广泛验证的QSAR模型的“良好做法”。.与实验研究者合作，将经验证的QSAR建模方法应用于药理学或药学意义的各种数据集;这一部分还包括开发有效数据分析和模型解释的新方法，这些方法基于数据压缩和从高维到低维描述符空间映射的高级算法。.在这项工作的过程中开发和验证的所有建模方法的实施，在公开访问的CNOQSAR网络服务器。该提案的成功实施预计将提供高度自动化，预测性和可访问的QSAR建模工具，这将有利于在药物设计和发现领域工作的广泛研究社区。

DESCRIPTION (provided by applicant): The main objective of this project is to develop, validate, and deliver efficient computational tools for rapid and reliable prediction of biological activity and/or related pharmaceutical properties of drug-like molecules. We plan to develop statistically significant and robust Quantitative Structure-Activity Relationships (QSAR) methodologies, which incorporate rigorous validation procedures and lead to models with a high predictive power and practical utility. The methodologies are built upon the similarity principle, i.e., similarity or diversity of chemical structures determines similarity or diversity of their biological action. We argue that chemical similarity should be evaluated in the context of the target property and employ objective similarity and diversity functions to achieve biologically meaningful clustering of compounds in the descriptor space. Consequently, our methodologies employ variable selection procedures aimed at identifying descriptors most relevant with respect to the target property. Paramount to our approach is rigorous model validation with external datasets which ensures the highest hit rates when predictive QSAR models are ultimately applied to screening chemical databases or virtual libraries for biologically active compounds. Four major areas of concentration in this proposal corresponding to its Specific Aims include: . development of novel, mainly non-linear QSAR methods, such as k nearest neighbor (kNN) and Support Vector Machines (SVM) approaches. The emphasis will be on the development of novel descriptors of chemical structure, and the efficiency, automation and statistical robustness of the underlying methodologies to afford their application to large commercial datasets. . development of efficient and objective QSAR model validation methodologies, which define the domain of model applicability and maximize the predictive ability of the models. This studies should lead to establishing widely accepted "good practices" of reliable and extensively validated QSAR models. . application of validated QSAR modeling methods to various datasets of pharmacological or pharmaceutical significance in collaboration with experimental investigators; this part also includes the development of new approaches for effective data analysis and model interpretation based on advanced algorithms for data compression and mapping from high- to low dimensional descriptor space. . implementation of all modeling methods developed and validated in the course of this work in the publicly accessible UNC QSAR web server. Successful implementation of this proposal is expected to afford highly automated, predictive and accessible QSAR modeling tools, which will benefit a broad research community working in the area of drug design and discovery.