Computational Approaches for Identifying Drugs Against Alzheimer's Disease - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Computational Approaches for Identifying Drugs Against Alzheimer's Disease write by Radha Mahendran. This book was released on 2017-03-23. Computational Approaches for Identifying Drugs Against Alzheimer's Disease available in PDF, EPUB and Kindle. Alzheimer’s disease is the most common form of dementia which is incurable. Although some kinds of memory loss are normal during aging, these are not severe enough to interfere with the level of function. ß-Secretase is an important protease in the pathogenesis of Alzheimer’s disease. Some statine-based peptidomimetics show inhibitory activities to the ß-secretase. To explore the inhibitory mechanism, molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies on these analogues were performed. Quantitative structure-activity relationship (QSAR) modeling pertains to the construction of predictive models of biological activities as a function of structural and molecular information of a compound library. The concept of QSAR has typically been used for drug discovery and development and has gained wide applicability for correlating molecular information with not only biological activities but also with other physicochemical properties, which has therefore been termed quantitative structure-property relationship (QSPR). In this study, 3D QSAR and pharmacophore mapping studies were carried out using Accelrys Discovery Studio 2.1. The best nine drugs were selected from the 16 ligands and pharmacophore features were generated.
Computational Modeling of Drugs Against Alzheimer’s Disease
Computational Modeling of Drugs Against Alzheimer’s Disease - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Computational Modeling of Drugs Against Alzheimer’s Disease write by Kunal Roy. This book was released on 2023-06-30. Computational Modeling of Drugs Against Alzheimer’s Disease available in PDF, EPUB and Kindle. This second edition volume expands on the previous edition with updated descriptions on different computational methods encompassing ligand-based, structure-based, and combined approaches with their recent applications in anti-Alzheimer drug design. Different background topics like recent advancements in research on the development of novel therapies and their implications in the treatment of Alzheimer’s Disease (AD) have also been covered for completeness. Special topics like basic information science methods for insight into neurodegenerative pathogenesis, drug repositioning and network pharmacology, and online tools to predict ADMET behavior with reference to anti-Alzheimer drug development have also been included. In the Neuromethods series style, chapter include the kind of detail and key advice from the specialists needed to get successful results in your laboratory. Cutting-edge and thorough, Computational Modeling of Drugs Against Alzheimer’s Disease, Second Edition is a valuable resource for all researchers and scientists interested in learning more about this important and developing field.
Drug Repurposing and Computational Drug Discovery
Drug Repurposing and Computational Drug Discovery - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Drug Repurposing and Computational Drug Discovery write by Mithun Rudrapal. This book was released on 2023-10-27. Drug Repurposing and Computational Drug Discovery available in PDF, EPUB and Kindle. Drug repurposing is defined as identifying new pharmacological indications from old, existing, failed, investigational, already marketed, or FDA-approved drugs and prodrugs, and applying these new uses in the treatment of diseases other than the drug’s original intended therapeutic use. The application of computational techniques in discovery research not only helps in the development of drugs from leads or existing drug molecules but can also be useful for the repurposing of existing drug candidates. This new volume presents exciting recent advances in drug repurposing and computational approaches for the discovery and development of drugs against certain difficult-to-treat and life-threatening diseases. With contributions from a global team of experts (academicians, scientists, and researchers), it explores the sophisticated tools and techniques of drug repurposing and computational drug discovery. It delivers valuable information on computational techniques, tools, and databases being utilized for drug repurposing and for identifying the uses of existing drug candidates on different emerging or deadly diseases. Drug repurposing and computational approaches addressed in the book target the discovery and development of drugs for microbial infections (bacterial, fungal, viral, COVID-19), parasitic diseases and neglected tropical diseases (NTDs), malignant diseases (cancer), inflammatory diseases, cardiovascular disorders, diabetes, and aging and neurological (CNS) disorders. In addition, the challenges and regulatory issues encountered in drug repurposing and computational drug discovery programs are looked at, offering perspectives for future directions.
Computational Approaches in Drug Discovery and Precision Medicine
Computational Approaches in Drug Discovery and Precision Medicine - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Computational Approaches in Drug Discovery and Precision Medicine write by Zunnan Huang. This book was released on 2021-03-15. Computational Approaches in Drug Discovery and Precision Medicine available in PDF, EPUB and Kindle.
Integrated Computational Drug Discovery Approaches for Neuropsychiatric Disorders
Integrated Computational Drug Discovery Approaches for Neuropsychiatric Disorders - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Integrated Computational Drug Discovery Approaches for Neuropsychiatric Disorders write by Mengshi Zhou. This book was released on 2020. Integrated Computational Drug Discovery Approaches for Neuropsychiatric Disorders available in PDF, EPUB and Kindle. Neuropsychiatric disorders (NPDs) such as Alzheimer's disease (AD) lead to enormous societal burdens. Current treatments of NPDs have limitations, and the drug development process is at a standstill. In this dissertation, we proposed integrated computational approaches to facilitate NPDs drug discovery. First, we developed data-driven system approaches to identify FDA-approved drugs that may target NPDs-related genes. We developed a novel network-based model for drug-target interaction (DTI) prediction by preserving context-specific drug-side effect relationships. Our new network model improved DTI prediction comparing to the traditional similarity-based network model. Furthermore, we extended our model by modeling 855,904 phenotypic and genetic relationships among 24,600 biomedical entities and constructed a DTI prediction system (TargetPredict). Next, TargetPredict was used to identify the FDA-approved drugs that may target AD-associated genes. The AD drugs identified by TargetPredict were associated with lower risks of AD and dementia in electronic health record (EHR) data of 17 million patients over 65 years old. Second, we developed a phenome-driven computational drug repositioning approach to identify NPDs treatments without known NPDs-related genes. Our approach hypothesized that similar drugs treat the same diseases. We applied the approach to Drug addiction (DA), assuming that new treatments share similar phenotypes and common targets with drugs that cause or treat DA. Our method could prioritize FDA-approved and not-yet-approved DA drugs. The top-ranked DA drug candidates may play a beneficial role regarding remission from dependence in 326,340 opioid-dependent patients' EHR data. The pathway-enrichment analysis supported this clinical observation. Third, we performed an EHRs-based retrospective case-control study of 56 million adults (age ≥ 18 years) to study the relationships between tumor necrosis factor (TNF)-mediated systemic inflammation and AD, and the potential of using anti-TNF drugs as AD treatments. We found the co-morbid inflammatory disease involving TNF was associated with an increased risk of AD, and this could be mitigated from the treatment of a TNF blocking agent. In conclusion, our studies, including computational drug target prediction, drug repositioning, and retrospective clinical corroboration, can rapidly identify anti-NPDs drug candidates. Those drug candidates will allow biomedical researchers to conduct hypothesis-driven functional studies in experimental models for NPDs.
