標題: Titlebook: Current Trends in Computational Modeling for Drug Discovery; Supratik Kar,Jerzy Leszczynski Book 2023 The Editor(s) (if applicable) and Th [打印本頁] 作者: 口語 時間: 2025-3-21 16:20
書目名稱Current Trends in Computational Modeling for Drug Discovery影響因子(影響力)
書目名稱Current Trends in Computational Modeling for Drug Discovery影響因子(影響力)學科排名
書目名稱Current Trends in Computational Modeling for Drug Discovery網絡公開度
書目名稱Current Trends in Computational Modeling for Drug Discovery網絡公開度學科排名
書目名稱Current Trends in Computational Modeling for Drug Discovery被引頻次
書目名稱Current Trends in Computational Modeling for Drug Discovery被引頻次學科排名
書目名稱Current Trends in Computational Modeling for Drug Discovery年度引用
書目名稱Current Trends in Computational Modeling for Drug Discovery年度引用學科排名
書目名稱Current Trends in Computational Modeling for Drug Discovery讀者反饋
書目名稱Current Trends in Computational Modeling for Drug Discovery讀者反饋學科排名
作者: Delude 時間: 2025-3-21 22:11
https://doi.org/10.1007/978-3-658-10567-9y the long process of drug design and discovery, and to optimize the selection of preferable features present in a new pharmaceutical. In this new vision, a more holistic approach can apply multiple methodologies and not only the screening of the adverse effects.作者: Filibuster 時間: 2025-3-22 00:42
Computational Toxicological Aspects in Drug Design and Discovery, Screening Adverse Effects,y the long process of drug design and discovery, and to optimize the selection of preferable features present in a new pharmaceutical. In this new vision, a more holistic approach can apply multiple methodologies and not only the screening of the adverse effects.作者: Enervate 時間: 2025-3-22 06:02 作者: 魅力 時間: 2025-3-22 09:13 作者: 預示 時間: 2025-3-22 13:12 作者: 預示 時間: 2025-3-22 20:40
https://doi.org/10.1007/978-3-658-10567-9ter, we provide an overview of computational SBDD workflow, and the various challenges associated with it. We also discuss strategies that could be adopted to tackle the challenges by making the best use of available information.作者: 偽書 時間: 2025-3-23 00:15 作者: constitute 時間: 2025-3-23 02:30
SBDD and Its Challenges,ter, we provide an overview of computational SBDD workflow, and the various challenges associated with it. We also discuss strategies that could be adopted to tackle the challenges by making the best use of available information.作者: abstemious 時間: 2025-3-23 05:32
In Silico Discovery of Class IIb HDAC Inhibitors: The State of Art,in silico studies including the virtual screening approaches have been implemented to design HDAC6 and HDAC10 inhibitors. In addition, the interactions of class IIb HDACs with their inhibitors are also highlighted extensively to get a detail insight. This chapter offers understanding for designing newer class IIb HDAC inhibitors in future.作者: 使苦惱 時間: 2025-3-23 10:46
Current Trends in Computational Modeling for Drug Discovery作者: 作嘔 時間: 2025-3-23 17:34
,Role of Computational Modeling in Drug Discovery for Alzheimer’s Disease,he three acetylcholinesterase inhibitors (AChEIs), and memantine an .-methyl-.-aspartate receptor (NMDAR) antagonist. These drugs are used mainly to alleviate mild cognitive impairment (MCI) providing temporary relief from the symptoms. This chapter discusses about the application of various computa作者: Infant 時間: 2025-3-23 21:28 作者: Contracture 時間: 2025-3-24 00:58
Targeted Computational Approaches to Identify Potential Inhibitors for Nipah Virus,r dynamics, integrated structure- and network-based approach, Drug–target–drug network-based approach, etc. In conclusion, this work will be helpful for the researchers in examining antivirals against NiV.作者: 石墨 時間: 2025-3-24 04:27
Role of Computational Modelling in Drug Discovery for HIV, and ligand-based computational methods is presented first; this is followed by some notable applications of these methods in the discovery of novel anti-HIV compounds. Finally, we discuss the emergence of powerful machine learning algorithms which have proven useful both in the design of new compou作者: grieve 時間: 2025-3-24 09:22 作者: 吸引力 時間: 2025-3-24 14:45
Databases for Drug Discovery and Development, new drug, utilizing existing chemical and drug databases for virtual screening makes the process faster as the database chemicals are already synthesized (in most cases) and characterized. Even in a few instances, absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles are ch作者: Medicare 時間: 2025-3-24 16:24 作者: 凹處 時間: 2025-3-24 22:17
https://doi.org/10.1007/978-3-658-10567-9he three acetylcholinesterase inhibitors (AChEIs), and memantine an .-methyl-.-aspartate receptor (NMDAR) antagonist. These drugs are used mainly to alleviate mild cognitive impairment (MCI) providing temporary relief from the symptoms. This chapter discusses about the application of various computa作者: 滑動 時間: 2025-3-25 02:38
https://doi.org/10.1007/978-3-658-10567-9ive immunosenescence and finding host genetic factors to expand the knowledge of infectious disease to an unprecedented level of detail. In addition to the fundamental molecular aspects of viral infection, this chapter emphasizes the fundamentals of computer modeling and discusses the relationship b作者: Devastate 時間: 2025-3-25 03:57
https://doi.org/10.1007/978-3-658-10567-9r dynamics, integrated structure- and network-based approach, Drug–target–drug network-based approach, etc. In conclusion, this work will be helpful for the researchers in examining antivirals against NiV.作者: hair-bulb 時間: 2025-3-25 10:29 作者: capsaicin 時間: 2025-3-25 15:33
https://doi.org/10.1007/978-3-658-10567-9thus can efficiently be used for data gap filling. The authors at the DTC Laboratory have developed a Java-based Read-Across tool (.) which utilizes three different similarity-based approaches (Euclidean Distance-based, Gaussian Kernel Similarity-based and Laplacian Kernel Similarity-based) for the 作者: grovel 時間: 2025-3-25 18:21 作者: 名詞 時間: 2025-3-25 20:51 作者: 遺留之物 時間: 2025-3-26 02:20
SBDD and Its Challenges,, novel, potent, and safe modulators. It is a joint effort from structural biologists and computational scientists, which considers various limitations of the techniques and suitably guides drug designers. Identifying a novel, potent, and safe drug-like molecule is a long challenging path, and throu作者: VICT 時間: 2025-3-26 05:43 作者: grovel 時間: 2025-3-26 09:56 作者: excursion 時間: 2025-3-26 14:52 作者: EXCEL 時間: 2025-3-26 18:17
Targeted Computational Approaches to Identify Potential Inhibitors for Nipah Virus,igh fatality rate. With time, the world has faced numerous outbreaks in various regions such as Malaysia, Bangladesh, Philippines, and India. In this chapter, we have summarized experimentally tested antivirals and computational approaches to predict potential inhibitors against NiV. Various studies作者: 糾纏 時間: 2025-3-27 00:26 作者: CANE 時間: 2025-3-27 04:05
Recent Insight of the Emerging Severe Fever with Thrombocytopenia Syndrome Virus: Drug Discovery, Td a tick-borne virus. Replication of SFTSV into systemic circulation and occurrence of viremia cause cytokine storm and T-cell overstimulation. The event of viremia-induced thrombocytopenia causes reduced platelet count and splenic macrophages, followed by endothelial damages and compromised immune 作者: ATRIA 時間: 2025-3-27 05:55 作者: visceral-fat 時間: 2025-3-27 11:21
Read-Across and RASAR Tools from the DTC Laboratory,pects like reproducibility, less ethical complications, no animal use and reduced time are some of the reasons why?researchers nowadays are shifting toward the in silico approaches for prediction. Quantitative Structure–Activity Relationship (QSAR) is one of the most commonly used in silico approach作者: ETCH 時間: 2025-3-27 15:29 作者: 喪失 時間: 2025-3-27 20:14
https://doi.org/10.1007/978-3-031-33871-7Drug design and discovery; QSAR Model; Computational Chemistry; Medicinal Chemistry; Computational Biolo作者: 諂媚于性 時間: 2025-3-28 01:17 作者: Cpr951 時間: 2025-3-28 04:38 作者: idiopathic 時間: 2025-3-28 09:56
Challenges and Advances in Computational Chemistry and Physicshttp://image.papertrans.cn/d/image/241437.jpg作者: Urologist 時間: 2025-3-28 14:05
https://doi.org/10.1007/978-3-658-10567-9, novel, potent, and safe modulators. It is a joint effort from structural biologists and computational scientists, which considers various limitations of the techniques and suitably guides drug designers. Identifying a novel, potent, and safe drug-like molecule is a long challenging path, and throu作者: 盟軍 時間: 2025-3-28 17:06
https://doi.org/10.1007/978-3-658-10567-9gical and pathological disease conditions. HDAC6 and HDAC10 are involved in different signaling pathways associated with several neurological disorders, various cancers at early as well as advanced stages, rare diseases, immunological conditions, etc. Thus, targeting these two enzymes has been found作者: aggravate 時間: 2025-3-28 19:18 作者: 脊椎動物 時間: 2025-3-29 00:37
https://doi.org/10.1007/978-3-658-10567-9antiviral drugs for treatment. Since the 1950s, new viral illnesses including AIDS, Hepatitis, and coronavirus infections like SARS, MERS, and COVID-19 have periodically emerged, posing a challenge to the development of antiviral drugs. The creation of computer models is an interactive, iterative pr作者: 組成 時間: 2025-3-29 05:50
https://doi.org/10.1007/978-3-658-10567-9igh fatality rate. With time, the world has faced numerous outbreaks in various regions such as Malaysia, Bangladesh, Philippines, and India. In this chapter, we have summarized experimentally tested antivirals and computational approaches to predict potential inhibitors against NiV. Various studies作者: gangrene 時間: 2025-3-29 10:12
https://doi.org/10.1007/978-3-658-10567-9nti-HIV drugs remains a major cause of concern, necessitating a regimen of highly active antiretroviral therapy (HAART), which consists of a combination of multiple drugs for long-term clinical benefit. Clearly, the rapid development of novel molecules that can help change the present regimen to new作者: gerrymander 時間: 2025-3-29 15:08 作者: Favorable 時間: 2025-3-29 16:43 作者: 編輯才信任 時間: 2025-3-29 21:28
https://doi.org/10.1007/978-3-658-10567-9pects like reproducibility, less ethical complications, no animal use and reduced time are some of the reasons why?researchers nowadays are shifting toward the in silico approaches for prediction. Quantitative Structure–Activity Relationship (QSAR) is one of the most commonly used in silico approach
