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
基本信息
- 批准号:10674729
- 负责人:
- 金额:$ 85.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:Adenosine A1 ReceptorAgonistAgrochemicalsAlgorithmsAndrogen ReceptorAnimal ModelAromataseAwardBayesian MethodBayesian ModelingBehaviorBiotechnologyChemicalsChemistryClientCollaborationsCollectionComputer ModelsComputer softwareDNADataData SetDatabasesDecision TreesEndocrine disruptionEstrogen ReceptorsFee-for-Service PlansFingerprintFoundationsFutureGenerationsGrantGraphIn VitroIndustryLaboratoriesLearningLettersLibrariesLicensingMachine LearningMarketingMeasuresMedicalMethodsModelingMolecularMorphologyPaperPathway interactionsPharmacologic SubstancePhaseProgress ReportsPropertyProteinsPublic DomainsPublishingReceiver Operating CharacteristicsSourceStructureSystemTestingToxic effectToxicologyTrainingValidationVisualizationWorkZebrafishadverse outcomecheminformaticsclassification algorithmcommercializationcomputational toxicologyconsumer productcostdashboarddata modelingdata visualizationdevelopmental toxicitydiverse datadrug discoverydrug induced liver injuryin vitro Assayin vitro testingin vivoin vivo Modelin vivo evaluationknowledge graphlarge datasetsmachine learning algorithmmachine learning modelmodel buildingmodel developmentmortalitymultitaskneurotoxicitynovelpostersprospectiveprototypepublic databaserandom forestregression algorithmscreeningtoolweb appweb site
项目摘要
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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
SEAN EKINS其他文献
SEAN EKINS的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('SEAN EKINS', 18)}}的其他基金
Preclinical development of a Nipah Virus inhibitor
尼帕病毒抑制剂的临床前开发
- 批准号:
10761349 - 财政年份:2023
- 资助金额:
$ 85.5万 - 项目类别:
New therapeutic approaches to identifying molecules for opioid abuse treatment
识别阿片类药物滥用分子的新治疗方法
- 批准号:
10385998 - 财政年份:2022
- 资助金额:
$ 85.5万 - 项目类别:
Machine learning approaches to predict Acetylcholinesterase inhibition
预测乙酰胆碱酯酶抑制的机器学习方法
- 批准号:
10378934 - 财政年份:2021
- 资助金额:
$ 85.5万 - 项目类别:
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:
10094026 - 财政年份:2020
- 资助金额:
$ 85.5万 - 项目类别:
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 - 财政年份:2019
- 资助金额:
$ 85.5万 - 项目类别:
MegaTrans – human transporter machine learning models
MegaTrans — 人类运输机机器学习模型
- 批准号:
9768844 - 财政年份:2019
- 资助金额:
$ 85.5万 - 项目类别:
MegaPredict for predicting natural product uses and their drug interactions
MegaPredict 用于预测天然产物用途及其药物相互作用
- 批准号:
10055938 - 财政年份:2019
- 资助金额:
$ 85.5万 - 项目类别:
Manufacture of an intracerebroventricular Enzyme Replacement Therapy for CLN1 Batten Disease
CLN1巴顿病脑室内酶替代疗法的研制
- 批准号:
10483470 - 财政年份:2018
- 资助金额:
$ 85.5万 - 项目类别:
Manufacture of an intracerebroventricular Enzyme Replacement Therapy for CLN1 Batten Disease
CLN1巴顿病脑室内酶替代疗法的研制
- 批准号:
10641950 - 财政年份:2018
- 资助金额:
$ 85.5万 - 项目类别:
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 - 财政年份:2017
- 资助金额:
$ 85.5万 - 项目类别:
相似国自然基金
Agonist-GPR119-Gs复合物的结构生物学研究
- 批准号:32000851
- 批准年份:2020
- 资助金额:24.0 万元
- 项目类别:青年科学基金项目
相似海外基金
S1PR1 agonistによる脳血液関門制御を介した脳梗塞の新規治療法開発
S1PR1激动剂调节血脑屏障治疗脑梗塞新方法的开发
- 批准号:
24K12256 - 财政年份:2024
- 资助金额:
$ 85.5万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
AHR agonistによるSLE皮疹の新たな治療薬の開発
使用 AHR 激动剂开发治疗 SLE 皮疹的新疗法
- 批准号:
24K19176 - 财政年份:2024
- 资助金额:
$ 85.5万 - 项目类别:
Grant-in-Aid for Early-Career Scientists
Evaluation of a specific LXR/PPAR agonist for treatment of Alzheimer's disease
特定 LXR/PPAR 激动剂治疗阿尔茨海默病的评估
- 批准号:
10578068 - 财政年份:2023
- 资助金额:
$ 85.5万 - 项目类别:
AUGMENTING THE QUALITY AND DURATION OF THE IMMUNE RESPONSE WITH A NOVEL TLR2 AGONIST-ALUMINUM COMBINATION ADJUVANT
使用新型 TLR2 激动剂-铝组合佐剂增强免疫反应的质量和持续时间
- 批准号:
10933287 - 财政年份:2023
- 资助金额:
$ 85.5万 - 项目类别:
Targeting breast cancer microenvironment with small molecule agonist of relaxin receptor
用松弛素受体小分子激动剂靶向乳腺癌微环境
- 批准号:
10650593 - 财政年份:2023
- 资助金额:
$ 85.5万 - 项目类别:
AMPKa agonist in attenuating CPT1A inhibition and alcoholic chronic pancreatitis
AMPKa 激动剂减轻 CPT1A 抑制和酒精性慢性胰腺炎
- 批准号:
10649275 - 财政年份:2023
- 资助金额:
$ 85.5万 - 项目类别:
Investigating mechanisms underpinning outcomes in people on opioid agonist treatment for OUD: Disentangling sleep and circadian rhythm influences on craving and emotion regulation
研究阿片类激动剂治疗 OUD 患者结果的机制:解开睡眠和昼夜节律对渴望和情绪调节的影响
- 批准号:
10784209 - 财政年份:2023
- 资助金额:
$ 85.5万 - 项目类别:
A randomized double-blind placebo controlled Phase 1 SAD study in male and female healthy volunteers to assess safety, pharmacokinetics, and transient biomarker changes by the ABCA1 agonist CS6253
在男性和女性健康志愿者中进行的一项随机双盲安慰剂对照 1 期 SAD 研究,旨在评估 ABCA1 激动剂 CS6253 的安全性、药代动力学和短暂生物标志物变化
- 批准号:
10734158 - 财政年份:2023
- 资助金额:
$ 85.5万 - 项目类别:
A novel nanobody-based agonist-redirected checkpoint (ARC) molecule, aPD1-Fc-OX40L, for cancer immunotherapy
一种基于纳米抗体的新型激动剂重定向检查点 (ARC) 分子 aPD1-Fc-OX40L,用于癌症免疫治疗
- 批准号:
10580259 - 财政年份:2023
- 资助金额:
$ 85.5万 - 项目类别:
Identification and characterization of a plant growth promoter from wild plants: is this a novel plant hormone agonist?
野生植物中植物生长促进剂的鉴定和表征:这是一种新型植物激素激动剂吗?
- 批准号:
23K05057 - 财政年份:2023
- 资助金额:
$ 85.5万 - 项目类别:
Grant-in-Aid for Scientific Research (C)