Artificial Intelligence Toolkit for Predicting Mixture Toxicity

化学品安全评估通常是针对个别化学品进行的。然而，工业化学品很少 单独行动会产生不良影响，因此混合物毒性评估是一项复杂但更多的 缓解环境化学品安全担忧的现实方法。有一个令人兴奋和高度 开发使用现代人工智能算法的创新方法以提供准确的 从混合物的化学成分预测其毒性，包括评估协同效应。 我们最近成立了Predictive，LLC，以支持商业和监管部门的开发和分销 预测重要毒性终点的强度模型。在这个第一阶段的STTR应用中，我们建议建立 一种基于网络的新型PreMixT(混合物毒性预测)工具包，建立在用于(I)数据收集的最佳实践的基础上， 清洁、协调和整合，(2)使用当前和新兴的人工智能方法和 混合模型前瞻性验证的深思熟虑策略，以及(Iii)特定终点毒性的预测 适用于纯化学品和混合物。我们将通过以下具体目标实现这一目标。 具体目标1：收集、整理和整合最大的可公开获得的混合物毒性数据集。我们 将探索所有可公开获取的关于混合物毒性的数据。初始数据集将包括急性口服毒性、急性 吸入毒性、急性皮肤毒性、皮肤致敏、皮肤刺激性和腐蚀性、眼睛刺激性和 腐蚀终点(统称为“6粒”)以及杀虫剂。我们还将收集和管理 未经测试的化学品和具有已知成分的环境问题混合物的数据集，如高 REACH数据库中的生产量(HPV)化学品和注册物质。数据将是 (重新)按照定制程序为化学信息学分析进行结构化、协调和准备。特定的 目的2：建立混合物毒性的人工智能模型。使用目标1中准备的数据，我们将严格开发 与环境健康风险相关的几个选定终点混合物毒性的验证模型 评估。我们将使用两种特定于混合物的描述符：分子的单纯形表示 结构(SiRMS)和混合图卷积描述符。建模方法将包括这两种常见方法 (例如，随机森林)以及创新的图卷积网络(GCN)方法。具体目标3. 开发PreMixT工具包和门户网站，支持化学品及其混合物的毒性预测。 我们将把精选数据和经过验证的模型集成到PreMixT Web应用程序中。此PreMixT服务器将 能够根据知识预测混合物的毒性，包括混合物成分的可能协同作用 在混合物中发现和表征的化学物质。成功完成我们的第一阶段研究将导致 将PreMixT网络应用程序开发为评估混合物毒性的集中资源， 包括混合物组分之间的协同作用。

Chemical safety assessment is typically conducted for individual chemicals. However, industrial chemicals rarely act in isolation to produce adverse effects, so mixture toxicity assessment represents a complex but more realistic approach to alleviating environmental chemical safety concerns. There is an exciting and highly impactful challenge to develop innovative approaches employing modern AI algorithms to provide accurate toxicity prediction of mixtures from their chemical composition, including the assessment of synergistic effects. We recently formed Predictive, LLC, to enable the development and distribution of commercial and regulatory strength models to predict important toxicity endpoints. In this Phase I STTR application, we propose to establish a novel web based PreMixT (Predictor of Mixture Toxicity) toolkit built on best practices for (i) data collection, cleaning, harmonization, and integration, (ii) model development using current and emerging AI approaches and thoughtful strategies of prospective validation of mixture models, and (iii) prediction of specific endpoint toxicities for both pure chemicals and mixtures. We will achieve this objective by focusing on the following Specific Aims. Specific Aim 1: Collect, curate, and integrate the largest publicly available mixture toxicity datasets. We will explore all the publicly accessible data on mixture toxicity. Initial datasets will include acute oral toxicity, acute inhalation toxicity, acute dermal toxicity, skin sensitization, skin irritation and corrosion, and eye irritation and corrosion endpoints (collectively known as "6-pack") as well as pesticides. We will also collect and curate datasets of untested chemicals and mixtures of the environmental concern with known composition such as High Production Volume (HPV) chemicals and registered substances in the REACH database. The data will be (re)structured, harmonized, and prepared for cheminformatics analysis following custom procedures. Specific Aim 2: Develop AI Models of mixture toxicity. Using data prepared in Aim 1, we will develop rigorously validated models of several selected endpoint mixture toxicities of relevance to environmental health risk assessment. We will employ two types of mixture-specific descriptors: Simplex Representation of Molecular Structure (SiRMS) and mixture graph convolution descriptors. Modeling approaches will include both common (e.g., Random Forest) as well as innovative Graph Convolutional Networks (GCN) approaches. Specific Aim 3. Develop the PreMixT toolkit and portal supporting the toxicity prediction of chemicals and their mixtures. We will integrate curated data and validated models into the PreMixT web application. This PreMixT server will be able to predict mixture toxicity, including possible synergy of mixture components, based on the knowledge of chemicals found and characterized in the mixture. Successful completion of our Phase I studies will result in the development of the PreMixT web application as a centralized resource to evaluate mixture toxicity, including the synergy between mixture components.