MegaTox for analyzing and visualizing data across different screening systems

MegaTox 用于分析和可视化不同筛选系统的数据

• 批准号: 10470050
• 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/10470050
• 关键词: Adenosine A1 Receptor; Agonist; Agrochemicals; Algorithms; Androgen Receptor; Animal Model; Aromatase; Award; Bayesian Method; Bayesian Modeling; Behavior; Biotechnology; Chemicals; Chemistry; Client; Collection; Computer Models; Computer software; Consult; 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; 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; Work; Zebrafish; adverse outcome; base; cheminformatics; classification algorithm; computational toxicology; consumer product; cost; dashboard; 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 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 计算毒理学的目的是根据特定端点的先前数据使用规则，模型和算法， 为了预测新分子是否可能会产生相似的负债。在某些情况下， 计算模型来自离散的分子终点（例如雌激素受体激动剂），而在 其他人的范围很广（例如药物诱导的肝损伤，DILI）。取得了很大进展 在模型开发和可用性的十年来计算毒理学中 生成更大的机器学习（ML）模型将进一步关注体外和体内测试 验证选定预测。药品，消费产品，农化学和其他化学反应 公司拥有数十年来筛选生成的结构活性数据，而不是在公开场合 域，这些数据主要仅适用于每个公司的化学信息学专家。外面 这些公司小型制药，生物技术公司和学者必须依靠公众的数据 数据库，商业数据库及其自己的数据。整合来自潜水资源的数据，并 使用算法来构建机器学习（ML）模型，可以帮助您对新的预测进行预测 化合物是一项巨大的事业。在该项目的第一阶段以开发Megatoxò的原型，我们策划了 然后，毒性数据集生成并测试了200多个最初集中在贝叶斯方法上的ML模型。 我们还开发了了解培训和测试集适用性并最终执行的方法 针对多个毒性靶标的前瞻性预测。完成了这些目标后，我们还与 许多学术实验室，并与五家商业公司进行了费用的服务。我们 目前有几家制药，农业和消费品公司评估我们的 许可之前的计算毒性模型。与潜在客户的讨论影响了这一点 第二阶段提议包括以下目的：1。比较和整合基于图形的新型模型，例如 图形与我们的15种不同ML回归和分类算法的套件用于建模毒理学 数据集，例如第阶段I中生成的数据集 根据需要使用DILI和其他毒性模型的计算模型。 3。生成经过验证的ML模型 从非哺乳动物物种的体内数据（最初使用斑马鱼），该数据将在体外和体内实现 相关性，可以有效地验证相对成本。在这一建议中，我们期望开发 具有15种不同算法的模型至少100个体外和体内数据集，导致> 1500毒性ML 型号。我们不知道任何其他公司都采用这种方法来产生新的高价值 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。 Megatoxò将是药品，消费产品，农业化学和 监管组以及用于支付服务咨询的监管组。