SBIR Phase I: Identifying Drug Leads via 3D Pharmacophore Space Analysis

SBIR 第一阶段：通过 3D 药效团空间分析识别先导药物

• 批准号: 1143484
• 负责人: Christian Lang
• 金额: $ 15万
• 依托单位: Acelot, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-01-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1143484&HistoricalAwards=false
• 关键词: SBIR; Phase; Identifying; Drug; Leads

This Small Business Innovation Research (SBIR) Phase I project will demonstrate that chemical compound activity/binding prediction can be significantly improved by applying geometric mining to the Joint Pharmacophore Space (JPS). Activity/binding prediction is of immense importance to the pharmaceutical industry as it allows evaluating biological properties early on in the drug development process. The proposed technique applies novel 3D clustering and statistical analysis to model configurations contributing/preventing binding between potentially large numbers of compounds and pharmacophoric targets (the JPS) and uses these models to search for other compounds with similar activity. In addition, the technique allows the interactive study of ?binding clusters? in order to design new compounds with specific binding properties and physicochemical properties.The broader/commercial impacts of this research are (1) improved efficiency of drug discovery pipelines, opening new opportunities, accelerating time-to-market of new drugs, and cutting R&D costs for pharmaceutical and biotech companies, and (2) the furthering of the understanding of basic compound properties, such as blood-brain-barrier permeability. This could have far-reaching consequences in the fight against diseases. Yet another outcome of the successful completion of this project is (3) the specific identification of new lead compounds antagonizing the toxic compounds causing Alzheimer's. Consequently, the project has a significant market potential as well as important ramifications for the improvement of human health.

这个小企业创新研究（SBIR）第一阶段项目将证明，通过将几何挖掘应用于联合药效团空间（JPS），可以显着改善化合物活性/结合预测。活性/结合预测对制药行业非常重要，因为它可以在药物开发过程的早期评估生物学特性。所提出的技术应用新的3D聚类和统计分析模型配置有助于/防止潜在的大量化合物和药效靶点（JPS）之间的结合，并使用这些模型来搜索具有类似活性的其他化合物。此外，该技术允许的互动研究？绑定集群？该研究的更广泛/商业影响是（1）提高药物发现管道的效率，开辟新的机会，加快新药的上市时间，并降低制药和生物技术公司的研发成本，以及（2）进一步了解基本化合物特性，如血脑屏障渗透性。这可能对防治疾病产生深远影响。该项目成功完成的另一个成果是（3）具体鉴定了拮抗导致阿尔茨海默氏症的有毒化合物的新先导化合物。因此，该项目具有巨大的市场潜力，并对改善人类健康产生重要影响。