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https://www.selleckchem.com/products/d-4476.html nts.By combining Machine Learning (ML) methods with Perturbation Theory (PT), it is possible to develop predictive models for a variety of response targets. Such combination often known as Perturbation Theory Machine Learning (PTML) modeling comprises a set of techniques that can handle various physical, and chemical properties of different organisms, complex biological or material systems under multiple input conditions. In so doing, these techniques effectively integrate a manifold of diverse chemical and biological data into a single computational framework that can then be applied for screening lead chemicals as well as to find clues for improving the targeted response(s). PTML models have thus been extremely helpful in drug or material design efforts and found to be predictive and applicable across a broad space of systems. After a brief outline of the applied methodology, this work reviews the different uses of PTML in Medicinal Chemistry, as well as in other applications. Finally, we cover the development of software available nowadays for setting up PTML models from large datasets.Alzheimer's Disease (AD) is a devastating neurodegenerative disease that affects millions of people in the world. The abnormal aggregation of amyloid β protein (Aβ) is regarded as the key event in AD onset. Meanwhile, the Aβ oligomers are believed to be the most toxic species of Aβ. Recent studies show that the Aβ dimers, which are the smallest form of Aβ oligomers, also have the neurotoxicity in the absence of other oligomers in physiological conditions. In this review, we focus on the pathogenesis, structure and potential therapeutic molecules against small Aβ oligomers, as well as the nanoparticles (NPs) in the treatment of AD. In this review, we firstly focus on the pathogenic mechanism of Aβ oligomers, especially the Aβ dimers. The toxicity of Aβ dimer or oligomers, which attributes to the interactions with various receptors and

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