Novel deep learning strategy to better predict pharmacological properties of candidate drugs and focus discovery efforts
新颖的深度学习策略可以更好地预测候选药物的药理学特性并集中发现工作
基本信息
- 批准号:10004481
- 负责人:
- 金额:$ 74.99万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-08-01 至 2022-03-31
- 项目状态:已结题
- 来源:
- 关键词:AnimalsAreaBenchmarkingBiological AssayChemical StructureChemicalsClassificationComputer ModelsComputer softwareConsumptionDataDescriptorDevelopmentDiseaseDrug KineticsFailureGoalsImageIntuitionLaboratoriesLanguageLibrariesMethodologyModelingMolecularMolecular StructureOutputPathway interactionsPerformancePermeabilityPharmaceutical ChemistryPharmaceutical PreparationsPharmacologic SubstancePharmacologyPhasePlayProcessPropertyQuantitative Structure-Activity RelationshipResearchResearch PersonnelRoleRunningScientistSeriesSolubilityStructureTechniquesTechnologyTherapeuticTimeTrainingTranslationsValidationVariantabsorptionautoencoderbasechemical propertycheminformaticscomputational chemistrycomputerized toolsdeep learningdeep neural networkdrug candidatedrug discoveryexperiencefeedingimprovedinnovationinterestlead candidatelead serieslearning strategymeltingmodel buildingneural networknovelnovel strategiesnovel therapeuticspredictive modelingscreeningvectorvoice recognition
项目摘要
PROJECT SUMMARY
Collaborative Drug Discovery, Inc. (CDD) proposes to continue development of a novel approach based on
deep learning neural networks to encode molecules into chemically rich vectors. In Phase 1 we demonstrated
that this representation enables computational models that more accurately predict the chemical properties of
molecules than state-of-the-art models, yet are also far simpler to build because they do not require any expert
decisions or optimization to achieve high performance. In Phase 2 we will exploit this unprecedented simplicity
to develop an intuitive software package that will for the first time enable any chemist or biologist working in
drug discovery to create and run their own predictive models – without relying on specialized cheminformatics
expertise – yet still achieve or exceed the accuracy of the best currently available techniques. Scientists engaged
in drug discovery research from academic laboratories to large pharmaceutical companies rely on
computational QSAR models to predict pharmacologically relevant properties and obviate the need to perform
expensive, time-consuming assays (many of which require animal studies) for every molecule of interest.
Improved models will enable researchers to select lead candidate series more effectively, explore chemical
space around leads to generate novel IP more efficiently, reduce failure rates for compounds advancing
through the drug discovery pipeline, and accelerate the entire drug discovery process. These benefits will be
realized broadly across most therapeutic areas.
We also plan to take the technology one step further, leveraging our chemically rich vector representation to
enable the software to creatively suggest novel compounds (which do not appear in the training libraries,
screening libraries, or lead series) that outperform the lead candidates simultaneously on bioactivity,
ADME/Tox and PK assays . Solving this inverse problem is the Holy Grail of computational medicinal
chemistry and has the potential to revolutionize drug discovery.
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项目总结
项目成果
期刊论文数量(0)
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会议论文数量(0)
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{{ truncateString('BARRY A BUNIN', 18)}}的其他基金
Automated Molecular Identity Disambiguator (AutoMID)
自动分子身份消歧器 (AutoMID)
- 批准号:
10357906 - 财政年份:2020
- 资助金额:
$ 74.99万 - 项目类别:
Automated Molecular Identity Disambiguator (AutoMID)
自动分子身份消歧器 (AutoMID)
- 批准号:
10569639 - 财政年份:2020
- 资助金额:
$ 74.99万 - 项目类别:
Intelligent Chemical Structure Browser for Drug Discovery and Optimization
用于药物发现和优化的智能化学结构浏览器
- 批准号:
10241834 - 财政年份:2019
- 资助金额:
$ 74.99万 - 项目类别:
A Robust, Secure Framework to Effortlessly Bind Distributed Databases and Analysis Tools into Tightly Integrated Translational Drug Discovery Computational Platforms
一个强大、安全的框架,可以轻松地将分布式数据库和分析工具绑定到紧密集成的转化药物发现计算平台中
- 批准号:
10484172 - 财政年份:2019
- 资助金额:
$ 74.99万 - 项目类别:
Digital representation of chemical mixtures to aid drug discovery and formulation
化学混合物的数字表示以帮助药物发现和配制
- 批准号:
9902210 - 财政年份:2019
- 资助金额:
$ 74.99万 - 项目类别:
A Robust, Secure Framework to Effortlessly Bind Distributed Databases and Analysis Tools into Tightly Integrated Translational Drug Discovery Computational Platforms
一个强大、安全的框架,可以轻松地将分布式数据库和分析工具绑定到紧密集成的转化药物发现计算平台中
- 批准号:
10685358 - 财政年份:2019
- 资助金额:
$ 74.99万 - 项目类别:
Intelligent Chemical Structure Browser for Drug Discovery and Optimization
用于药物发现和优化的智能化学结构浏览器
- 批准号:
10386918 - 财政年份:2019
- 资助金额:
$ 74.99万 - 项目类别:
Novel deep learning strategy to better predict pharmacological properties of candidate drugs and focus discovery efforts
新颖的深度学习策略可以更好地预测候选药物的药理学特性并集中发现工作
- 批准号:
10133177 - 财政年份:2018
- 资助金额:
$ 74.99万 - 项目类别:
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