MegaTox for analyzing and visualizing data across different screening systems

MegaTox 用于分析和可视化不同筛查系统的数据

• 批准号: 10674729
• 负责人: SEAN EKINS
• 金额: $ 85.5万
• 依托单位: COLLABORATIONS PHARMACEUTICALS, INC.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10674729
• 关键词: Adenosine A1 Receptor; Agonist; Agrochemicals; Algorithms; Androgen Receptor; Animal Model; Aromatase; Award; Bayesian Method; Bayesian Modeling; Behavior; Biotechnology; Chemicals; Chemistry; Client; Collaborations; Collection; Computer Models; Computer software; DNA; Data; Data Set; Databases; Decision Trees; Endocrine disruption; Estrogen Receptors; Fee-for-Service Plans; Fingerprint; Foundations; Future; Generations; Grant; Graph; In Vitro; Industry; Laboratories; Learning; Letters; Libraries; Licensing; Machine Learning; Marketing; Measures; Medical; Methods; Modeling; Molecular; Morphology; Paper; Pathway interactions; Pharmacologic Substance; Phase; Progress Reports; Property; Proteins; Public Domains; Publishing; Receiver Operating Characteristics; Source; Structure; System; Testing; Toxic effect; Toxicology; Training; Validation; Visualization; Work; Zebrafish; adverse outcome; cheminformatics; classification algorithm; commercialization; computational toxicology; consumer product; cost; dashboard; data modeling; data visualization; developmental toxicity; diverse data; drug discovery; drug induced liver injury; in vitro Assay; in vitro testing; in vivo; in vivo Model; in vivo evaluation; knowledge graph; large datasets; machine learning algorithm; machine learning model; model building; model development; mortality; multitask; neurotoxicity; novel; posters; prospective; prototype; public database; random forest; regression algorithm; screening; tool; web app; web site

Project Summary Computational toxicology aims to use rules, models and algorithms based on prior data for specific endpoints, to enable the prediction of whether a new molecule will possess similar liabilities or not. In some cases, the computational models are derived from discrete molecular endpoints (e.g. estrogen receptor agonism) while in others they are quite broad in scope (e.g. drug induced liver injury, DILI). Considerable progress has been made in computational toxicology in a decade both in model development and availability such that the latest generation of larger scale machine learning (ML) models will further focus in vitro and in vivo testing on verification of select predictions. Pharmaceutical, consumer products, agrochemical and other chemistry focused companies possess structure-activity data generated over many decades of screening that is not in the public domain, and this data is primarily only accessible to the cheminformatics experts in each company. Outside of these companies small pharmaceutical, biotech companies and academics must rely on data from public databases, commercial databases and their own data. Integrating such data from diverse sources and processing with algorithms to build machine learning (ML) models that can help to enable predictions for new compounds is a vast undertaking. Over Phase I of this project to develop the prototype for MegaToxÒ, we curated toxicity datasets then generated and tested well over 200 ML models initially focused on the Bayesian approach. We have also developed approaches to understand training and test set applicability and ultimately performed prospective predictions against several toxicity targets. Having completed these aims, we also collaborated with numerous academic laboratories and performed fee-for-service work with five commercial companies. We currently have several pharmaceutical, agrochemical and consumer product companies evaluating our computational toxicity models prior to licensing. These discussions with potential customers have influenced this Phase II proposal to include the following aims: 1. Compare and integrate novel graph-based models such as graphSAGE versus our suite of 15 different ML regression and classification algorithms for modeling toxicology datasets such as those generated in Phase I. 2. Integrate read across and adverse outcome pathway methods with our computational models for DILI and other toxicity models as needed. 3. Generate validated ML models from in vivo data for non-mammalian species (initially using Zebrafish) which will enable in vitro and in vivo correlations and can be validated relatively cost effectively. In this proposal over 2 years we expect to develop models with 15 different algorithms for at least 100 in vitro and in vivo datasets, leading to > 1500 toxicity ML models. We are not aware of any other company pursuing such an approach to both generate new high value datasets or models, performing testing of their own models and creating a wide array of toxicity ML models. MegaToxÒ will be a product available for licensing by pharmaceutical, consumer product, agrochemical and regulatory groups as well as used in fee-for-service consulting.

项目摘要 计算毒理学旨在使用基于特定终点先前数据的规则、模型和算法， 从而能够预测一个新的分子是否具有类似的负债。在一些情况下的 计算模型来自离散的分子终点（例如雌激素受体激动）， 其他则范围相当广泛（例如药物性肝损伤，DILI）。取得了相当大的进展 在计算毒理学方面，无论是在模型开发还是可用性方面， 更大规模的机器学习（ML）模型的产生将进一步关注体外和体内测试， 选择预测的验证。医药、消费品、农用化学品和其他化学品 公司拥有几十年筛选产生的结构-活性数据，这些数据不公开 这些数据主要只有每个公司的化学信息学专家才能访问。之外 这些小型制药公司、生物技术公司和学术机构必须依赖公共数据， 数据库、商业数据库和自己的数据。整合来自不同来源的此类数据， 使用算法进行处理，以构建机器学习（ML）模型，这些模型可以帮助预测新的 化合物是一项巨大的工程。在这个项目的第一阶段，我们策划了MegaToxidine的原型开发， 然后生成并测试了超过200个最初专注于贝叶斯方法的ML模型的毒性数据集。 我们还开发了理解训练和测试集适用性的方法，并最终执行了 针对几个毒性目标的前瞻性预测。在完成这些目标后，我们还与 他还与许多学术实验室合作，并与五家商业公司开展收费服务工作。我们 目前有几家制药、农业化学品和消费品公司正在评估我们的 计算毒性模型之前，许可证。这些与潜在客户的讨论影响了这一点 第二阶段建议包括以下目标：1.比较和集成新的基于图形的模型，例如 graphSAGE与我们用于毒理学建模的15种不同ML回归和分类算法套件 数据集，如第一阶段生成的数据集。2.整合交叉解读和不良结局途径方法 我们的DILI计算模型和其他毒性模型。3.生成经过验证的ML模型 来自非哺乳动物物种的体内数据（最初使用斑马鱼），这将使体外和体内 相关性，并且可以相对成本有效地进行验证。在这两年多的时间里，我们希望开发出 具有15种不同算法的模型，用于至少100个体外和体内数据集，导致> 1500个毒性ML 模型据我们所知，没有任何其他公司采用这种方法来创造新的高价值 数据集或模型，对自己的模型进行测试，并创建各种毒性ML模型。 MegaToxidine将是一种可供制药、消费品、农用化学品和 监管团体以及用于收费服务咨询。