Biocomputation across distributed private datasets to enhance drug discovery

跨分布式私有数据集的生物计算以增强药物发现

• 批准号: 8590552
• 负责人: SEAN EKINS
• 金额: $ 46.29万
• 依托单位: COLLABORATIVE DRUG DISCOVERY, INC.
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-16 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8590552
• 关键词: Algorithms; Archives; Biological Assay; Biotechnology; Cells; Chemical Structure; Chemistry; Collaborations; Computer Analysis; Computer Simulation; Computer software; Data; Data Collection; Data Set; Descriptor; Development; Disease; Ensure; Event; Exhibits; Foundations; Government; Infectious Diseases Research; Inhibitory Concentration 50; Investments; Laboratories; Lead; Libraries; Malaria; Modeling; Mycobacterium tuberculosis; Online Systems; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Privacy; Property; Psyche structure; PubChem; Quantitative Structure-Activity Relationship; Rare Diseases; Research; Research Institute; Research Personnel; Resources; Scientist; Series; Small Business Innovation Research Grant; Source; System; Technology; Test Result; Time; Training; Tuberculosis; Validation; Vendor; Vision; Work; base; computerized tools; cytotoxicity; data sharing; drug discovery; expectation; improved; neglect; novel; open source; pre-clinical; prospective; prototype; public health relevance; repository; research study; screening; success; tool; tuberculosis drugs; virtual

DESCRIPTION: Collaborative Drug Discovery, Inc. (CDD) will create a novel web-based software platform that enables scientists to work together effectively to discover and improve new drug leads by sharing computational predictions based on open-source descriptors and models, for the first time without needing to reveal underlying chemical structures and biodata. It will create the first practical system of bio computational 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 bio computational 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 II of this SBIR project, CDD will: 1. Create a stand-alone platform, based entirely on open source technologies, that enables researchers to share models, share predictions from models, and create models from distributed, heterogeneous QSAR data - all without needing to divulge the underlying training sets. 2. Develop approaches that enable scientists who are not computational chemists to exploit the technology. A series of user interfaces will automate and intelligently guide the user to create or exploit models and assist the user to visualize domains of applicability, interpret results, and understand their limitations. The integrated platforms will enable scientists to seamlessly create, share and execute computational models leveraging private data vaults, with or without sharing the underlying training data. 3. Validate the platform by (a) developing a suite of at least five ADME/Tox and physicochemical property models based on open-source descriptors and data obtained from commercial ADME vendors, as well as public data from PubChem, ChEMBL and other sources, (b) securely making available a series of sophisticated pre- competitive ADME/Tox models provided by large pharmaceutical companies, and (c) demonstrating that col- laboratory can utilize the platform on their own (without relying on a computational chemist) to discover and advance TB drug leads with good ADME/Tox properties.

名称：Collaborative Drug Discovery，Inc. (CDD)将创建一个新的基于网络的软件平台，使科学家能够有效地合作，通过共享基于开源描述符和模型的计算预测，首次发现和改进新药先导，而无需揭示潜在的化学结构和生物数据。它 将创建第一个跨不同所有者的分布式数据集进行生物计算分析的实用系统，同时尊重数据隐私。通过降低这一合作的关键障碍，该平台将加速临床前药物发现管道。针对被忽视的疾病和孤儿适应症的研究将特别受益，因为它们通常依赖于学术研究人员、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）的松散联系。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综合工作流程（其中数据可以在各部门之间自由共享）。该项目将首次使专注于被忽视疾病和孤儿适应症的研究人员能够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。它还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大型高质量数据集的重大投资中受益。在SBIR项目的第二阶段，CDD将：1。创建一个完全基于开源技术的独立平台，使研究人员能够共享模型、共享模型预测以及从分布式、异构QSAR数据创建模型-所有这些都无需泄露底层训练集。2.开发方法，使科学家谁不是计算化学家利用技术。一系列的用户界面将自动化和智能地引导用户创建或利用模型，并帮助用户可视化适用性领域，解释结果并了解其局限性。集成平台将使科学家能够利用私有数据库无缝创建、共享和执行计算模型，无论是否共享底层训练数据。3.通过（a）开发一个套件 至少五种ADME/Tox和物理化学性质模型，其基于开源描述符和从商业ADME供应商获得的数据，以及来自PubChem、ChEMBL和其他来源的公开数据，（B）安全地提供由大型制药公司提供的一系列复杂的竞争前ADME/Tox模型，以及（c）证明合作实验室可以自己利用该平台（不依赖于计算化学家）来发现和推进具有良好ADME/Tox性质的TB药物先导物。