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- Volume 20, Issue 3, 2019
Current Drug Metabolism - Volume 20, Issue 3, 2019
Volume 20, Issue 3, 2019
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Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design
Authors: Zhongyan Li, Qingqing Miao, Fugang Yan, Yang Meng and Peng ZhouBackground: Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention. Methods: We present a systematic review on the application of machin Read More
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Targeting Virus-host Protein Interactions: Feature Extraction and Machine Learning Approaches
Authors: Nantao Zheng, Kairou Wang, Weihua Zhan and Lei DengBackground: Targeting critical viral-host Protein-Protein Interactions (PPIs) has enormous application prospects for therapeutics. Using experimental methods to evaluate all possible virus-host PPIs is labor-intensive and time-consuming. Recent growth in computational identification of virus-host PPIs provides new opportunities for gaining biological insights, including applications in disease control. We provide an overview of recen Read More
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Survey of Machine Learning Techniques in Drug Discovery
Authors: Natalie Stephenson, Emily Shane, Jessica Chase, Jason Rowland, David Ries, Nicola Justice, Jie Zhang, Leong Chan and Renzhi CaoBackground: Drug discovery, which is the process of discovering new candidate medications, is very important for pharmaceutical industries. At its current stage, discovering new drugs is still a very expensive and time-consuming process, requiring Phases I, II and III for clinical trials. Recently, machine learning techniques in Artificial Intelligence (AI), especially the deep learning techniques which allow a computational model to Read More
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Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction
Authors: Wen Zhang, Weiran Lin, Ding Zhang, Siman Wang, Jingwen Shi and Yanqing NiuBackground: The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods. Results: In the paper, we review the recent advances in machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and su Read More
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Molecular Design of Peptide-Fc Fusion Drugs
Authors: Lin Ning, Bifang He, Peng Zhou, Ratmir Derda and Jian HuangBackground: Peptide-Fc fusion drugs, also known as peptibodies, are a category of biological therapeutics in which the Fc region of an antibody is genetically fused to a peptide of interest. However, to develop such kind of drugs is laborious and expensive. Rational design is urgently needed. Methods: We summarized the key steps in peptide-Fc fusion technology and stressed the main computational resources, tools, and meth Read More
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A Review of Recent Advances and Research on Drug Target Identification Methods
Authors: Yang Hu, Tianyi Zhao, Ningyi Zhang, Ying Zhang and Liang ChengBackground: From a therapeutic viewpoint, understanding how drugs bind and regulate the functions of their target proteins to protect against disease is crucial. The identification of drug targets plays a significant role in drug discovery and studying the mechanisms of diseases. Therefore the development of methods to identify drug targets has become a popular issue. Methods: We systematically review the recent work o Read More
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The Development of Machine Learning Methods in Cell-Penetrating Peptides Identification: A Brief Review
Authors: Huan-Huan Wei, Wuritu Yang, Hua Tang and Hao LinBackground: Cell-penetrating Peptides (CPPs) are important short peptides that facilitate cellular intake or uptake of various molecules. CPPs can transport drug molecules through the plasma membrane and send these molecules to different cellular organelles. Thus, CPP identification and related mechanisms have been extensively explored. In order to reveal the penetration mechanisms of a large number of CPPs, it is nec Read More
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Recent Advances in Machine Learning Methods for Predicting Heat Shock Proteins
Authors: Wei Chen, Pengmian Feng, Tao Liu and Dianchuan JinBackground: As molecular chaperones, Heat Shock Proteins (HSPs) not only play key roles in protein folding and maintaining protein stabilities, but are also linked with multiple kinds of diseases. Therefore, HSPs have been regarded as the focus of drug design. Since HSPs from different families play distinct functions, accurately classifying the families of HSPs is the key step to clearly understand their biological functions. In contr Read More
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Survey of Machine Learning Techniques for Prediction of the Isoform Specificity of Cytochrome P450 Substrates
Authors: Yi Xiong, Yanhua Qiao, Daisuke Kihara, Hui-Yuan Zhang, Xiaolei Zhu and Dong-Qing WeiBackground: Determination or prediction of the Absorption, Distribution, Metabolism, and Excretion (ADME) properties of drug candidates and drug-induced toxicity plays crucial roles in drug discovery and development. Metabolism is one of the most complicated pharmacokinetic properties to be understood and predicted. However, experimental determination of the substrate binding, selectivity, sites and rates of metabolism i Read More
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Recent Advances on Prediction of Human Papillomaviruses Risk Types
Authors: Yuhua Yao, Huimin Xu, Manzhi Li, Zhaohui Qi and Bo LiaoBackground: Some studies have shown that Human Papillomavirus (HPV) is strongly associated with cervical cancer. As we all know, cervical cancer still remains the fourth most common cancer, affecting women worldwide. Thus, it is both challenging and essential to detect risk types of human papillomaviruses. Methods: In order to discriminate whether HPV type is highly risky or not, many epidemiological and experimental met Read More
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Iron and Vitamin D/Calcium Deficiency after Gastric Bypass: Mechanisms Involved and Strategies to Improve Oral Supplement Disposition
Authors: Aisling Mangan, Carel W. Le Roux, Nana G. Miller and Neil G. DochertyBackground: Nutritional deficiencies are common following Roux-en-Y Gastric Bypass (RYGB). Aetiology is diverse; including non-compliance, altered diet, unresolved preoperative deficiency and differential degrees of post-operative malabsorption occurring as function of length of bypassed intestine. Iron and calcium/vitamin D deficiency occur in up to 50% of patients following RYGB. Currently, treatment strategies recomme Read More
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Volumes & issues
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Volume 25 (2024)
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Volume 24 (2023)
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Volume 23 (2022)
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Volume 22 (2021)
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Volume 21 (2020)
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Volume 20 (2019)
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Volume 19 (2018)
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Volume 18 (2017)
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Volume 17 (2016)
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Volume 16 (2015)
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Volume 15 (2014)
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Volume 14 (2013)
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Volume 13 (2012)
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Volume 12 (2011)
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Volume 11 (2010)
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Volume 10 (2009)
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Volume 9 (2008)
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Volume 8 (2007)
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Volume 7 (2006)
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Volume 6 (2005)
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Volume 5 (2004)
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Volume 4 (2003)
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Volume 3 (2002)
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Volume 2 (2001)
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Volume 1 (2000)
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