权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
10094026
负责人：
SEAN EKINS
金额：
$ 12.49万
依托单位：
COLLABORATIONS PHARMACEUTICALS, INC.
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-05 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10094026
关键词：
Acetylcholinesterase Ache Address Adenosine Adenosine A1 Receptor Agonist Algorithms Anticonvulsants Chlorine Clinical Trials Communities Computer Models Data Data Set Databases Decision Trees Development FDA approved Funding Goals Grant In Vitro Licensing Logistic Regressions Lung Inflammation Machine Learning Modeling Pesticides Pharmaceutical Preparations Phase Physiological Privatization PubChem Small Business Innovation Research Grant Source Speed System Technology Testing Toxic effect Toxicology Transforming Growth Factor alpha Transforming Growth Factor beta base clinical candidate computational toxicology cost effectiveness deep neural network drug discovery in vitro Assay in vitro testing in vivo medical countermeasure nerve agent novel pesticide poisoning preclinical study predictive modeling pulmonary agents random forest screening support vector machine virtual

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. Our recent efforts have used sources like PubChem and ChEMBL to build predictive models for different toxicity-related and drug discovery endpoints. Our Phase I SBIR proposal called MegaTox will provide toxicity machine learning models developed with different algorithms for 40-50 in vitro and in vivo toxicity datasets. We propose using this technology to generate machine learning models for predicting potential compounds against either TGF- a target for countering chlorine induced lung inflammation as well as the adenosine A1 receptor to identify agonists as potential anticonvulsants. In addition, we can also compile molecules that can reactivate acetylcholinesterase which would enable the potential to discover medical countermeasures to address nerve agent and pesticide poisoning. We will access multiple machine learning approaches and validate these Bayesian or other machine learning models (including Linear Logistic Regression, AdaBoost Decision Tree, Random Forest, Support Vector Machine and deep neural networks (DNN) of varying depth) with our own in-house technology for these selected targets. We will aim for ROC values greater than 0.75 and MCC and F1 scores that are acceptable (>0.3). These models will be used to virtually screen FDA approved drugs, clinical candidates, commercially available drugs or other molecules. We will select up to 50 molecules to be tested using in vitro assays alongside controls for each target. These combined efforts should in the first instance provide commercially viable treatments which will be used to experimentally validate our computational models that can be shared with the medical countermeasures scientific community. In summary, we are proposing to build and validate models for targets based on public databases, select compounds for testing, create proprietary data and use this as a starting point for further optimization of compounds if needed. Our goal is to identify at least one promising compound for each target that we then pursue and protect our IP. We will pursue additional grant funding to take these medical countermeasures through additional in vitro and in vivo preclinical studies. Ultimately, we will license our products to larger companies for development prior to clinical trials.

项目总结

项目成果

期刊论文数量（27）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Defending Antiviral Cationic Amphiphilic Drugs That May Cause Drug-Induced Phospholipidosis.

DOI：
10.1021/acs.jcim.1c00903
发表时间：
2021-09-27
期刊：
Journal of chemical information and modeling
影响因子：
5.6
作者：
Lane TR;Ekins S
通讯作者：
Ekins S

Comparing Machine Learning Models for Aromatase (P450 19A1).

DOI：
10.1021/acs.est.0c05771
发表时间：
2020-12-01
期刊：
Environmental science & technology
影响因子：
11.4
作者：
Zorn KM;Foil DH;Lane TR;Hillwalker W;Feifarek DJ;Jones F;Klaren WD;Brinkman AM;Ekins S
通讯作者：
Ekins S

Erratum: CATMoS: Collaborative Acute Toxicity Modeling Suite.

DOI：
10.1289/ehp9883
发表时间：
2021-07
期刊：
Environmental health perspectives
影响因子：
10.4
作者：
Mansouri K;Karmaus A;Fitzpatrick J;Patlewicz G;Pradeep P;Alberga D;Alepee N;Allen TEH;Allen D;Alves VM;Andrade CH;Auernhammer TR;Ballabio D;Bell S;Benfenati E;Bhattacharya S;Bastos JV;Boyd S;Brown JB;Capuzzi SJ;Chushak Y;Ciallella H;Clark AM;Consonni V;Daga PR;Ekins S;Farag S;Fedorov M;Fourches D;Gadaleta D;Gao F;Gearhart JM;Goh G;Goodman JM;Grisoni F;Grulke CM;Hartung T;Hirn M;Karpov P;Korotcov A;Lavado GJ;Lawless M;Li X;Luechtefeld T;Lunghini F;Mangiatordi GF;Marcou G;Marsh D;Martin T;Mauri A;Muratov EN;Myatt GJ;Nguyen DT;Nicolotti O;Note R;Pande P;Parks AK;Peryea T;Polash A;Rallo R;Roncaglioni A;Rowlands C;Ruiz P;Russo D;Sayed A;Sayre R;Sheils T;Siegel C;Silva AC;Simeonov A;Sosnin S;Southall N;Strickland J;Tang Y;Teppen B;Tetko IV;Thomas D;Tkachenko V;Todeschini R;Toma C;Tripodi I;Trisciuzzi D;Tropsha A;Varnek A;Vukovic K;Wang Z;Wang L;Waters KM;Wedlake AJ;Wijeyesakere SJ;Wilson D;Xiao Z;Yang H;Zahoranszky-Kohalmi G;Zakharov AV;Zhang FF;Zhang Z;Zhao T;Zhu H;Zorn KM;Casey W;Kleinstreuer NC
通讯作者：
Kleinstreuer NC

The Antiviral Drug Tilorone Is a Potent and Selective Inhibitor of Acetylcholinesterase.