SBIR Phase I: A Learning Drug Discovery Platform in the Cloud

SBIR 第一阶段：云端学习药物发现平台

• 批准号: 1346176
• 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: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1346176&HistoricalAwards=false
• 关键词: SBIR; Phase; Learning; Drug; Discovery

This Small Business Innovation Research (SBIR) Phase I project proposes to develop a web-based learning software platform for collaborative drug discovery and optimization. Current drug discovery software tools do not support the sophisticated data management that is needed for iterative and adaptive drug discovery, learning, and collaboration. This project aims to change this by building a web-based drug discovery platform with three main objectives: (1) provide novel insightful graphical feedback on binding properties, (2) provide simple virtual screening over millions of vendor compounds, and (3) keep track of past assay results in order to iteratively refine prediction models and to continuously improve accuracy. The challenges in this project include (1) scalable data management to support hundreds of users and large numbers of compounds and models, (2) data encryption for the protection of proprietary information, and (3) a simple pharmacologist-oriented user interface. The outcome of the project will be a web-based tool that can be used across pharma/biotech research groups and across labs for effective collaborative drug discovery and optimization. The broader impact/commercial potential of this project, if successful, will be the potential for novel visualizations that will significantly improve the understanding of molecular binding properties. It will result in novel data mining and analysis algorithms that can operate directly on encrypted data, with far-reaching impact on other areas that require data safe-keeping (e.g., electronic health record management). In addition, the project will offer insights into interface design for pharmacologists, biologists, and chemists and how to better foster collaboration among them. On a larger scale, the project will aid in the discovery of novel leads for pressing healthcare needs. Especially for difficult drug targets without much structural information, such as Alzheimer's disease targets or cancer targets, the proposed platform can identify novel drug-leads with fewer side effects and higher efficacy. This ultimately may result in a much faster time-to-market for such drugs. For pharma/biotech companies using the platform developed in this project, this could mean a significant commercial advantage.

该小型企业创新研究 (SBIR) 第一阶段项目建议开发一个基于网络的学习软件平台，用于协作药物发现和优化。当前的药物发现软件工具不支持迭代和自适应药物发现、学习和协作所需的复杂数据管理。该项目旨在通过构建基于网络的药物发现平台来改变这一现状，该平台具有三个主要目标：(1) 提供有关结合特性的新颖、富有洞察力的图形反馈，(2) 提供对数百万种供应商化合物的简单虚拟筛选，以及 (3) 跟踪过去的测定结果，以便迭代完善预测模型并不断提高准确性。该项目面临的挑战包括（1）可扩展的数据管理，以支持数百个用户和大量化合物和模型，（2）用于保护专有信息的数据加密，以及（3）简单的面向药理学家的用户界面。该项目的成果将是一个基于网络的工具，可在制药/生物技术研究小组和实验室之间使用，以进行有效的协作药物发现和优化。如果成功，该项目更广泛的影响/商业潜力将是新颖的可视化的潜力，这将显着提高对分子结合特性的理解。它将带来新颖的数据挖掘和分析算法，可以直接对加密数据进行操作，对需要数据安全的其他领域（例如电子健康记录管理）产生深远的影响。此外，该项目还将为药理学家、生物学家和化学家的界面设计以及如何更好地促进他们之间的合作提供见解。从更大的范围来看，该项目将有助于发现新的线索，以满足紧迫的医疗保健需求。特别是对于没有太多结构信息的困难药物靶点，例如阿尔茨海默病靶点或癌症靶点，该平台可以识别副作用更少、疗效更高的新型先导药物。这最终可能会导致此类药物的上市时间更快。对于使用该项目开发的平台的制药/生物技术公司来说，这可能意味着巨大的商业优势。