Using Common Fund datasets for xenobiotic localization
Using Common Fund datasets for xenobiotic localization
批准号:
10357502
负责人:
Md Nurunnabi
金额:
$30.4万
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-09-22 至 2023-09-21
关键词:
AddressAlgorithmsAreaCell NucleusCellsChemotherapy-Oncologic ProcedureClinicalCodeCommon Data ElementConsensusDataData ElementData SetDescriptorDiseaseDocumentationDoseDrug Delivery SystemsDrug TargetingDrug resistanceDrug toxicityEventExposure toFailureFingerprintFluorescenceFunctional disorderFundingFunding OpportunitiesGenomeGlyburideGoalsHumanIn VitroKnowledgeLabelLeadLibrariesLigandsLocationLysosomesMachine LearningMediatingMicroscopicMicrotubulesMitochondriaModelingMulti-Drug ResistanceNetwork-basedOrganOutcomePaclitaxelPharmaceutical PreparationsPharmacologyPilot ProjectsPlayProcessProteinsPublic HealthResearchRiskRoleScientistSirolimusSiteSpecificityTechniquesTechnologyTestingTherapeuticTissuesToxic effectValidationWorkXenobioticsbasechemotherapycombinatorialcomputerized toolsdesigndosagedrug candidatedrug developmentdrug discoverydrug dispositionimprovedin vivometabolomicsneglectnovelopen sourcepharmacophorepredictive modelingprogramsradiotracerrefractory cancerscaffoldside effectsmall moleculesuccesstherapeutic effectivenesstooltool development
中文摘要
项目总结
英文摘要
Project Summary
Subcellular localization, such as the nucleus lysosomes, and mitochondria, has tremendous potential to
enhance the effectiveness of the therapeutic molecules rather than random distribution throughout the cell.
With improved subcellular localization and enhanced concentration, a specific molecule can be more
efficacious as well as less toxic which is usually a concern of random distribution and nonspecific localization.
Therefore, understanding subcellular distribution and the mechanism for a specific molecule can further
modulate subcellular dysfunction mediated diseases. Xenobiotic localization at the subcellular level has a
profound effect on several processes. The overarching goal of the proposed work is to develop a novel
platform with computational tools for specific xenobiotic localization. The proposed work will take advantage of
three common fund datasets. In specific aim-1, we aim to develop a suite of machine learning (ML) models for
hierarchical levels of micro-compartmentation and 40 specific subcellular locations. These machine learning
models will be first built using three different types of features (fingerprints-based, pharmacophore-based, and
physicochemical descriptors-based). Then, they are fused using an advanced multilayer combinatorial fusion
algorithm to get the best consensus model. We will also perform the scaffold analysis to identify critical
scaffolds that play a role in accumulating molecules at specific subcellular locations. In specific aim-2, we will
conduct experimental validation of the predictions developed ML models. More specifically we will test 50
compounds for their subcellular location. In specific aim-3, we plan to build an open portal that incorporates
datasets, ML model, prediction server, and documentation. All the data and models generated from the project
are made available as open-source.
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