MegaTox for analyzing and visualizing data across different screening systems

项目摘要 计算毒理学的目标是对特定的终端使用基于先前数据的规则、模型和算法， 从而能够预测一个新分子是否会有类似的负债。在某些情况下， 计算模型是从离散的分子端点(例如雌激素受体激动剂)派生出来的，而在 其他的则范围相当广(例如，药物引起的肝损伤，DILI)。已经取得了相当大的进展 在计算毒理学的十年中，无论是在模型开发还是可用性方面，最新的 更大规模的机器学习(ML)模型的生成将进一步专注于体外和体内测试 对精选预测的验证。专注于制药、消费品、农用化学品和其他化学产品 公司拥有几十年筛选过程中产生的结构活动数据，这些数据是不公开的 这些数据主要只有每家公司的化学信息学专家才能访问。在外部 这些公司小型制药公司、生物技术公司和学者必须依赖公共数据 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，并 使用算法进行处理以构建机器学习(ML)模型，该模型可以帮助实现对新的 化合物是一项巨大的事业。在这个项目的第一阶段，为了开发MegaTox的原型，我们策划了 然后，毒性数据集生成并测试了200多个最初侧重于贝叶斯方法的ML模型。 我们还开发了了解培训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，并与五家商业公司开展收费服务工作。我们 目前有几家制药、农用化学品和消费品公司正在评估我们的 许可前的计算毒性模型。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，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的对比 数据集，如在阶段I中生成的数据集。2.整合读取跨路径和不良结果路径方法 根据需要使用我们的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，并且可以相对经济有效地进行验证。在这份提案中，我们预计将在两年多的时间里 至少100个体外和体内数据集的15种不同算法的模型，导致&gt；1500毒性ML 模特们。据我们所知，没有任何其他公司寻求这样的方法来创造新的高价值 数据集或模型，执行自己的模型测试，并创建广泛的毒性ML模型。 MegaTox将是一种可用于制药、消费品、农用化学品和 监管团体以及收费服务咨询中使用的。

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.