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

这个小型企业创新研究(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。目前的药物发现软件工具不支持迭代和适应性药物发现、学习和协作所需的复杂数据管理。该项目旨在通过建立一个基于网络的药物发现平台来改变这种情况，主要目标有三个：(1)提供关于结合性质的新颖的有洞察力的图形反馈，(2)提供对数百万供应商化合物的简单虚拟筛选，以及(3)跟踪过去的检测结果，以便反复改进预测模型并不断提高准确性。该项目面临的挑战包括：(1)支持数百个用户和大量化合物和模型的可扩展数据管理；(2)用于保护专有信息的数据加密；(3)面向药理学家的简单用户界面。该项目的成果将是一个基于网络的工具，可用于跨制药/生物技术研究小组和跨实验室进行有效的协作药物发现和优化。该项目的更广泛的影响/商业潜力，如果成功，将是新的可视化的潜力，将显著提高对分子结合性质的理解。它将产生新的数据挖掘和分析算法，可以直接对加密数据进行操作，对其他需要数据安全保管的领域(例如电子健康记录管理)产生深远影响。此外，该项目将为药理学家、生物学家和化学家提供关于界面设计的见解，以及如何更好地促进他们之间的合作。在更大的范围内，该项目将有助于发现紧迫的医疗需求的新线索。特别是对于没有太多结构信息的困难药物靶点，如阿尔茨海默病靶点或癌症靶点，所提出的平台可以识别副作用更少、疗效更高的新药物线索。这最终可能导致此类药物更快地上市。对于使用该项目开发的平台的制药/生物技术公司来说，这可能意味着显著的商业优势。

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.