SBIR Phase I: The Development of Self-Organizing Maps for Drug Discovery

SBIR 第一阶段：药物发现自组织图的开发

• 批准号: 0319787
• 负责人: Jeffrey Schmitt
• 金额: $ 10万
• 依托单位: Targacept, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-01 至 2004-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0319787&HistoricalAwards=false
• 关键词: SBIR; Phase; Development; Self; Organizing

This Small Business Innovation Research Phase I project will test the feasibility of using Self-Organizing Maps (SOMs), a non-linear, topology-preserving pattern recognition technique, in the design of small molecules with biological activity. Due to a serious unmet need for computer-based tools that can accurately predict early development issues such as potency, safety, and efficacy of drug candidates, the pharmaceutical industry currently relies almost exclusively upon expensive prototyping to evaluate candidates. Many drug candidates fail only after costly pharmaceutics development and/or clinical trials, limiting the ability of drug companies to address anything beyond the most profitable markets. The early stage technology proposed in this Phase I project will significantly advance the state-of-the-art of drug design by addressing one of the major problems with which current computer-aided drug design has long struggled: how to model the non-linear feature-target relationships with dependent features commonly associated with biological systems.The commercial application of this project is in the area of drug discovery and development. The innovative software to be developed through this project will allow the drug companies to computationally evaluate a vast number of drug candidates, resulting in more candidates being investigated and more effective drugs discovered.

这个小企业创新研究第一阶段项目将测试使用自组织映射（SOM）的可行性，SOM是一种非线性，拓扑保持模式识别技术，在设计具有生物活性的小分子。 由于对基于计算机的工具的严重未满足的需求，这些工具可以准确地预测候选药物的效力，安全性和有效性等早期开发问题，制药行业目前几乎完全依赖于昂贵的原型来评估候选药物。 许多候选药物只是在昂贵的药物开发和/或临床试验之后才失败，这限制了制药公司在最有利可图的市场之外解决任何问题的能力。 在这个第一阶段项目中提出的早期阶段技术将通过解决当前计算机辅助药物设计长期以来一直在努力解决的主要问题之一，显著推进药物设计的最新技术：如何模拟具有通常与生物系统相关的依赖特征的非线性特征-靶标关系。 通过该项目开发的创新软件将使制药公司能够对大量候选药物进行计算评估，从而研究更多的候选药物并发现更有效的药物。