Centralized assay datasets for modelling support of small drug discovery organizations

用于小型药物发现组织建模支持的集中化分析数据集

• 批准号: 9751326
• 负责人: SEAN EKINS
• 金额: $ 69.28万
• 依托单位: COLLABORATIONS PHARMACEUTICALS, INC.
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9751326
• 关键词: Algorithm Design; Algorithms; Area; Artificial Intelligence; Back; Bayesian Modeling; Bayesian learning; Binding; Biological; Biological Assay; Caring; Catalogs; Cells; Chemistry; Client; Collaborations; Computer Simulation; Computer software; Consult; Cytochrome P450; Data; Data Quality; Data Set; Data Sources; Databases; Decision Trees; Descriptor; Development; Disease; Ebola virus; Employment; Ensure; Environment; Estrogen Receptors; Evaluation; FDA approved; Feedback; Fees; Fingerprint; Foundations; Growth; HIV; HIV/TB; Industry Standard; Intelligence; Intention; Java; Judgment; Knowledge; Lead; Leishmaniasis; Licensing; Linear Regressions; Link; Literature; Logistics; Machine Learning; Measurement; Measures; Metadata; Methods; Modeling; Modification; Molecular; Nuclear Receptors; Output; Pathway interactions; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Privatization; Process; Production; Property; Public Domains; Publications; RNA-Directed DNA Polymerase; Research; Research Personnel; Resources; Rights; Scientist; Series; Software Tools; Statistical Data Interpretation; Structure; Structure-Activity Relationship; Synthesis Chemistry; Technology; Testing; Toxic effect; Toxicology; Training Support; Triage; Tuberculosis; Update; Vendor; Visualization software; Work; base; computational suite; cost; data modeling; data submission; data visualization; deep learning; deep neural network; design; drug discovery; exhaustion; improved; learning strategy; machine learning algorithm; neglect; novel therapeutics; prospective; prototype; random forest; scale up; screening; small molecule; software development; statistics; success; text searching; tool; trend

Project Summary The growing importance of artificial intelligence (AI) is visible by the growth in companies and increasing deals over the past year between pharma and smaller companies using machine learning to assist in drug discovery. The continuing steady growth of structure-activity data for diverse targets, diseases and molecular properties poses a considerable challenge as they are generally not readily accessible for machine learning: content resides in a mixture of public databases (with differing levels of curation), disparate files within research groups, non- curated literature publications. In Phase I, Collaborations Pharmaceuticals Inc. developed a prototype of Assay Central software and used this with a wide variety of structure activity data from sources both public and private, formatted and unformatted, for enabling neglected, rare or common disease targets. Public data was mixed with collaborator/customer-contributed data, using original software and applied chemistry judgment of an expert team. In Phase I we created error checking and correction software. We also built and validated Bayesian models with the datasets that were collected and cleaned. And, in addition, we developed new data visualization tools. The software environment that we created readily enables the user to compile structure-activity data for building computational models and can be used to create selections of these models for sharing with collaborators as needed. This software can in turn be used for scoring new molecules and visualizing the multiple outputs in various formats. We have enabled ~14 collaborative projects which have shared models on specific targets such as PyrG for Tuberculosis (identifying a lead compound), HIV reverse transcriptase, whole cell screening for Leishmaniasis as well as P450 and nuclear receptor models (e.g. estrogen receptor) relevant to toxicology. We have utilized Assay Central in our ongoing internal projects working on Ebola, HIV and tuberculosis small molecule drug discovery. In Phase II, we propose the following aims that will enable us to develop Assay Central into a production tool for enabling drug discovery collaborations which we will continue to focus on. In Phase 1 we performed a preliminary analysis of different machine learning algorithms with select drug discovery datasets. In Phase II we will now perform a thorough evaluation and selection of additional machine learning algorithms and molecular descriptors as well as assessment of combination of algorithms (e.g. Bayesian and Deep Learning). We will implement disease/target definitions for machine learning models to facilitate drug discovery. We will enable molecule selection and automated design and optimization. The utility of having such a tool as Assay Central readily available will empower scientists to leverage public, private or a combination of data to help with their drug discovery tasks. Developing this software suite of computational models with public data will enable us to identify foundations, academics and potential collaborators that generate preliminary data to test models. These efforts will dramatically increase the number of projects we can work on, create new IP, and generate employment using machine learning focused on drug discovery in the area of rare and neglected diseases, in particular. Assay Central benefits include 1. Ease of deployment and use with a Java file executed by users without the need for IT support; 2. Built on industry standard technologies; 3. Graphical display of models provides instant feedback; 4 Model applicability with multiple methods to assess scores and graphics.

项目摘要 人工智能（AI）日益增长的重要性可以从公司的增长和交易的增加中看出 在过去的一年里，制药公司和小公司之间使用机器学习来帮助药物发现。 针对不同靶点、疾病和分子特性的构效数据的持续稳定增长 提出了相当大的挑战，因为它们通常不容易被机器学习访问：内容驻留在 在公共数据库（具有不同的策展级别）、研究组内的不同文件、非 策划文学出版物。在第一阶段，Collaborations Pharmaceuticals Inc.开发了一个分析的原型 中央软件，并使用这与各种各样的结构活动数据来源，无论是公共和私人， 格式化和非格式化，用于实现被忽视的、罕见的或常见的疾病目标。公开数据与 合作者/客户贡献的数据，使用原始软件和专家的应用化学判断 团队在第一阶段，我们创建了错误检查和纠正软件。我们还建立并验证了贝叶斯模型 与已收集和清理的数据集进行比较。此外，我们还开发了新的数据可视化工具。 我们创建的软件环境使用户能够轻松编译用于构建的结构-活性数据 计算模型，并且可以用于创建这些模型的选择，以与协作者共享， needed.该软件可以反过来用于对新分子进行评分，并将多个输出可视化， 各种格式。我们已经启动了约14个合作项目，这些项目在特定目标上共享模型， 作为结核病的PyrG（鉴定一种先导化合物），HIV逆转录酶，全细胞筛选 利什曼病以及与毒理学相关的P450和核受体模型（如雌激素受体）。我们 在我们正在进行的埃博拉病毒、艾滋病毒和结核病内部项目中， 分子药物发现 在第二阶段，我们提出了以下目标，使我们能够将Assay Central开发为生产工具 我们将继续关注的药物发现合作。在第一阶段，我们进行了一项 不同机器学习算法与选定药物发现数据集的初步分析。在第二阶段，我们 现在将对其他机器学习算法和分子进行全面的评估和选择， 描述符以及算法组合的评估（例如贝叶斯和深度学习）。我们将 实现机器学习模型的疾病/目标定义，以促进药物发现。我们将使 分子选择和自动化设计和优化。使用Assay Central等工具的效用 随时可用的数据将使科学家能够利用公共，私人或组合数据来帮助他们 药物发现任务。利用公共数据开发这种计算模型的软件套件将使我们能够 确定基金会、学者和潜在合作者，为测试模型提供初步数据。这些 这些努力将大大增加我们可以参与的项目数量，创造新的IP，并产生 使用机器学习的就业重点是罕见和被忽视疾病领域的药物发现， 特别的。分析中心的优势包括1.易于部署和使用由用户执行的Java文件 无需IT支持; 2.基于行业标准技术; 3。模型的图形显示 提供即时反馈; 4模型的适用性与多种方法来评估分数和图形。