Novel Schizophrenia Therapeutics By Virtual High-Throughput Screening

通过虚拟高通量筛选的新型精神分裂症治疗方法

• 批准号: 7361172
• 负责人: Jens Meiler
• 金额: $ 19.93万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-12-19 至 2009-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7361172
• 关键词: Adopted; Algorithms; Binding Sites; Biological; Biological Neural Networks; Blood - brain barrier anatomy; Chemical Structure; Chemicals; Cognitive; Collaborations; Complex; Computer software; Databases; Development; Disease; Education; Funding; Generations; Genetic; Genetic Programming; Half-Life; Hand; Internet; Investments; Lead; Libraries; Machine Learning; Maps; Membrane; Membrane Proteins; Metabotropic Glutamate Receptors; Methods; Mining; Modeling; Molecular Bank; Pharmaceutical Preparations; Principal Investigator; Property; Proteins; Purpose; Research; Research Personnel; Scheme; Schizophrenia; Screening Result; Screening procedure; Structure; System; Techniques; Therapeutic; United States National Institutes of Health; Work; base; chemical synthesis; cheminformatics; cognitive function; computerized tools; design; drug development; experience; high throughput screening; innovation; mathematical model; novel; pharmacophore; programs; protein structure; research study; small molecule; therapeutic target; tool; virtual

DESCRIPTION (provided by applicant): Selective potentiators of the metabotropic glutamate receptor subtype mGluR5 have exciting potential for development of novel treatment strategies for schizophrenia and other disorders that disrupt cognitive function. The latest generation of selective mGluR5 potentiators is based on the lead compound CDPPB and features systemically active compounds with long half-lives that cross the blood-brain barrier. A high-throughput screen (HTS) for mGluR5 potentiators at Vanderbilts NIH-funded molecular libraries screening center network facility revealed a large and diverse set of about 1400 substances whose activity was validated in independent experiments. The present ChemInformatics proposal targets utilizing the power of recent machine learning techniques such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) to model the complex relationship between chemical structure and biological activity of mGluR5 potentiators reflected in the HTS results. An innovative encoding scheme is developed that allows mapping of the diverse chemical space into a single mathematical model. The resulting Quantitative Structure Activity Relation (QSAR) models will serve a three-fold purpose: (a) a comprehensive binding site pharmacophore will be obtained to facilitate understanding of the SAR and rationalize further experiments; (b) the models will be used to virtually screen libraries of millions of compounds which are known but not physically available for HTS at Vanderbilt to gain a priority list for acquisition or synthesis; and (c) in combination with an existing Genetic Algorithm (GA) structure generator existing lead compounds will be optimized and new structures will be designed to identify potential new targets for synthesis. Overall we hope to not only identify novel allosteric potentiators of mGluR5 and understand their activity as potential treatment of schizophrenia and other disorders that disrupt cognitive function, but also to build an innovative ChemInformatics software and database tool which can be adopted for research in other NIH molecular libraries screening centers. The developed applications will be made freely and readily accessible for academic research using a WWW interface deeply integrated in the drug development pipeline. The employed QSAR models require no crystal structure of the target protein. Hence the method can be readily applied to membrane proteins-such as GPCRs-which are target of 40-50% of modern medicinal drugs. The PI of the proposal has extensive experience in the usage of ANNs and SVMs to predict properties of organic molecules and proteins (1-9), solve protein structures (10-15), and predict activity of therapeutics (16). He implemented GAs for the design and optimization of chemical structures (17,18). For the tasks at hand he teams up with Jeff Conn, a renowned expert for researching mGluRs (19) and potential therapeutics targeting these systems (20-22).

描述（由申请人提供）：代谢型谷氨酸受体亚型mGluR 5的选择性增效剂具有开发精神分裂症和其他破坏认知功能的疾病的新治疗策略的令人兴奋的潜力。最新一代的选择性mGluR 5增效剂基于先导化合物CDPPB，其特点是具有穿过血脑屏障的长半衰期的全身活性化合物。 在范德比尔特国家卫生研究院资助的分子库筛选中心网络设施的mGluR 5增效剂的高通量筛选（HTS）揭示了一个大的和多样化的约1400种物质，其活性在独立的实验中得到验证。目前的化学信息学提案的目标是利用最近的机器学习技术，如人工神经网络（ANN）和支持向量机（SVM）的力量来模拟HTS结果中反映的mGluR 5增效剂的化学结构和生物活性之间的复杂关系。一个创新的编码方案的开发，允许映射到一个单一的数学模型的不同的化学空间。由此产生的定量构效关系（QSAR）模型将用于三重目的：（a）将获得全面的结合位点药效团，以促进对SAR的理解并使进一步的实验合理化;（B）将使用模型来虚拟筛选数百万已知但在范德比尔特的HTS中物理上不可用的化合物的库，以获得用于获取或合成的优先列表;和（c）与现有的遗传算法（GA）结构生成器组合，现有的先导化合物将被优化，新的结构将被设计以鉴定潜在的新的合成靶。 总的来说，我们希望不仅能识别mGluR 5的新型变构增效剂，并了解它们作为精神分裂症和其他破坏认知功能的疾病的潜在治疗方法的活性，而且还能建立一个创新的ChemInformatics软件和数据库工具，可用于其他NIH分子库筛选中心的研究。开发的应用程序将使用深入集成在药物开发管道中的WWW界面免费和易于访问。所采用的QSAR模型不需要靶蛋白的晶体结构。因此，该方法可以很容易地应用于膜蛋白-如GPCR-这是40-50%的现代药物的目标。 该提案的PI在使用ANN和SVM预测有机分子和蛋白质的性质（1-9），解决蛋白质结构（10-15）和预测治疗活性（16）方面具有丰富的经验。他实现了遗传算法的化学结构的设计和优化（17，18）。对于手头的任务，他与Jeff Conn合作，Jeff Conn是研究mGluRs（19）和针对这些系统（20-22）的潜在疗法的着名专家。