Enhancing Drug Discovery Research by Free Energy Modeling
Enhancing Drug Discovery Research by Free Energy Modeling
批准号:
10730788
负责人:
Emilio Gallicchio
金额:
$45.59万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-09-25 至 2026-08-31
关键词:
AccelerationAddressAdoptedAlgorithmsAreaBenchmarkingBindingBiochemicalBiologyCellsChemicalsChemistryCommunitiesComplementComplexComputer ModelsComputer softwareComputersDevelopmentDevicesDisciplineDiseaseDissociationDockingDrug AddictionEducationEvolutionFosteringFree EnergyGoalsHealth SciencesHydration statusImprove AccessIndividualIndustrializationIonsLaboratoriesLibrariesLigand BindingLigandsMalignant NeoplasmsMarketingMedicineMetalsMethodsModelingModernizationMolecularMolecular ConformationMotionPharmaceutical PreparationsProteinsResearchResearch PersonnelResolutionScienceScreening procedureShapesSolidStructureStudentsSystemTechniquesTechnologyViralVirus DiseasesWatercancer addictioncellular targetingcollaborative environmentcollegecomputerized toolsdrug discoveryeffective therapyexperienceinsightlaboratory experimentmolecular dynamicsmolecular modelingmolecular recognitionnew technologynext generationnovelnovel therapeuticsparallel computerprogramsprotein complexreceptorsimulationskillstheoriestoolundergraduate researchundergraduate research experiencevirtual screening
中文摘要
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英文摘要
PROJECT SUMMARY/ABSTRACT
Accurate computational binding free energy models that estimate the dissociation constants of protein-
ligand complexes by representing the motion of compounds and their cellular targets at atomic
resolution are increasingly contributing to the discovery of drugs. However, due to their limited range
of applicability, perceived barriers to entry, and scarcity of computational modelers with sufficient
expertise to make good use of them, free energy models of protein-drug binding are still not widely
adopted, especially in academic settings. The project addresses the need to widen access, build
expertise, and extend binding free energy computational models for a broader pool of drug discovery
applications in academic and industrial settings. It does so while expanding the research capacity at
Brooklyn College by providing molecular modeling support to experimental projects in the health
sciences and offering meaningful educational and research opportunities to our students.
The PI’s lab has developed the Alchemical Transfer Method (ATM) for binding free energy
estimation that incorporates the best science and simplifications afforded by the next generation of
algorithms and circumvents many of the shortcomings of traditional approaches. ATM supports diverse
ligand libraries and will be extended to treat bridging water molecules, metal ions, and conformational
changes induced by ligand binding, all critical and outstanding problem areas for binding free energy
models. Through partnerships with local academic labs, the project will investigate viral, cancer, and
drug addiction targets enabled by these novel technologies.
Molecular modeling tools are only as good as their ability to give insights into the molecular
basis of disease. Building on solid partnerships with experimental laboratories, the project will promote
the complementary use of molecular modeling in biochemical and drug discovery research through
interdisciplinary student research experiences. It will build upon the successful ICompute
Interdisciplinary Undergraduate Research initiative at Brooklyn College to bring computational and
experimental laboratories together and nurture the next generation of researchers.
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