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• W2519085043 abstract "Positron emission tomography (PET) tracers play an important role in the diagnosis of Alzheimer's disease, a condition that leads to progressive dementia and memory loss. A high binding affinity and specificity of the PET tracers to amyloid oligomers and fibrils are crucial for their successful application as diagnostic agents. In this sense, it is essential to design PET tracers with enhanced binding affinities, which can lead to more precise and earlier detection of Alzheimer's disease conditions. The application of in silico methodology for the design and development of efficient PET tracers may serve as an important route to improved Alzheimer's disease diagnosis. In this work, the performance of widely used computational methods is explored for predicting experimental binding affinities of styrylbenzoxazole (SB) derivatives against a common amyloid protofibril. By performing docking, molecular dynamics, and quantum chemistry calculations in sequence their combined predictive performance is explored. The present work emphasizes the merits as well as limitations of these simulation strategies in the realm of designing PET tracers for Alzheimer's disease diagnosis." @default.
• W2519085043 created "2016-09-23" @default.
• W2519085043 creator A5001779926 @default.
• W2519085043 creator A5053665869 @default.
• W2519085043 creator A5078167731 @default.
• W2519085043 date "2016-09-29" @default.
• W2519085043 modified "2023-10-01" @default.
• W2519085043 title "Multistep Modeling Strategy To Improve the Binding Affinity Prediction of PET Tracers to Aβ42: Case Study with Styrylbenzoxazole Derivatives" @default.
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• W2519085043 doi "https://doi.org/10.1021/acschemneuro.6b00216" @default.
• W2519085043 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27626391" @default.
• W2519085043 hasPublicationYear "2016" @default.
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