Biocomputation across distributed private datasets to enhance drug discovery

DESCRIPTION（由申请人提供）：Collaborative Drug Discovery, Inc. （CDD）提议创建一个新颖的基于网络的软件平台，使科学家能够有效地一起工作，发现和改进新的药物先导，但可以选择不向彼此透露化学结构。它将在尊重数据隐私的同时，创建第一个跨不同所有者的分布式数据集的生物计算分析实用系统。通过降低这一关键的合作障碍，该平台将加速临床前药物发现管道。针对被忽视疾病和孤儿适应症的研究将特别受益，因为它们往往依赖于学术研究者、非营利基金会、政府实验室和小型生物技术公司（“非制药”实体）的松散合作。这些努力通常不仅缺乏资源，而且缺乏大型制药公司开展的发现项目的集成工作流程（在这些项目中，数据可以在各部门之间自由共享）。该项目将首次使研究人员能够专注于被忽视的疾病和孤儿适应症，有效地利用生物计算工具，如虚拟筛选和ADME/Tox预测，这些工具现在被认为是大型制药公司早期发现工作流程的标准和不可或缺的组成部分。这也将使这些非制药研究人员更容易与大型制药公司合作，并从大型制药公司积累的大量高质量数据集中受益。在拟议的SBIR的第一阶段，CDD将利用正在进行的合作来证明该方法的可行性和价值，并在实验确认之前进行潜在的效力预测。主要合作者包括威尔康奈尔医学院的Carl Nathan教授、NIAID的Clifton Barry博士和传染病研究所（IDRI）的Allen Casey。他们的小组将作为这个项目的试验台。它们都有正在进行的筛选项目，以发现对结核病有活性的化合物。第一阶段的具体目标包括：展示协作筛选中心创建基于分布式、异构数据集的计算结核筛选模型的价值，并前瞻性地利用这些模型来筛选和优先考虑计划筛选的分子。验证假设，通过选择富含活性化合物的亚群，这些中心可以有效地探索更多的化学空间，而不是用有限的资源。2. 开发用于指定模型的初始标准（包括目的、输入、输出、算法、描述符类型、适用性领域和其他表示、解释和交换所需的参数），这些模型将形成CDD将在阶段2中迭代开发、部署、测试和验证的更全面的软件原型的大纲。

DESCRIPTION (provided by applicant): Collaborative Drug Discovery, Inc. (CDD) proposes to create a novel web-based software platform that enables scientists to work together effectively to discover and improve new drug leads, yet with the option not to reveal chemical structures to each other. It will create the first practical system of biocomputational analysis across distributed datasets with different owners, while respecting data privacy. By lowering this key barrier to collaboration, the platform will accelerate the pre-clinical drug discovery pipeline. Research aimed at neglected diseases and orphan indications will especially benefit, because they often rely on the loosely affiliated efforts of academic investigators, non-profit foundations, government laboratories, and small biotechnology firms ("extra-pharma" entities). Such efforts typically lack not only the resources but also the integrated workflows of discovery projects conducted at large pharmaceutical companies (within which data can be shared freely across departments). The project will for the first time enable researchers focused on neglected diseases and orphan indications to effectively exploit biocomputational tools such as virtual screening and ADME/Tox predictions, which are now considered to be standard and indispensible components of early discovery workflows within large pharma. It will also make it easier for these extra-pharma researchers to collaborate with large pharma and benefit from large pharma's significant investment accumulating large high-quality datasets. In Phase 1 of the proposed SBIR, CDD will leverage ongoing collaborations to prove the feasibility and value of the approach with prospective potency predictions in advance of experimental confirmation. Key collaborators include Prof. Carl Nathan at Weill Cornell Medical College, Dr. Clifton Barry, III, at NIAID, and Allen Casey at the Infectious Disease Research Institute (IDRI). Their groups will serve as an experimental test bed for the project. They all have ongoing screening programs to discover compounds active against tuberculosis (TB). Specific aims for Phase 1 include: 1. Demonstrate the value to the collaborating screening centers of creating computational TB screening models derived from distributed, heterogeneous collections of data and exploiting the models prospectively to filter and prioritize the molecules scheduled to be screened. Validate the hypothesis that by selecting subsets enriched with active compounds, the centers can efficiently explore more of chemical space than would otherwise be possible with limited resources. 2. Develop initial standards for specifying models (including purpose, inputs, outputs, algorithms, descriptor types, domain of applicability and other parameters necessary for presentation, interpretation, and exchange) that will form the outline for more comprehensive software prototypes that CDD will iteratively develop, deploy, test and validate in Phase 2. PUBLIC HEALTH RELEVANCE: The proposed project will create novel computational tools that will help researchers to accelerate the discovery of new and improved drugs against a wide range of diseases. These tools will particularly benefit researchers working on diseases that leading pharmaceutical companies have largely ignored because they are not perceived as highly profitable opportunities, despite the fact that in many cases they afflict millions of people.