Abstract:
INTRODUCTION:Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates. MATERIALS AND METHODS:In this study, we developed a novel method for predicting Cell-Penetrating Peptides (CPPs) membrane penetrating capability. For this, we used orthogonal encoding to encode amino acid and each amino acid position as one variable. Then a software of IBM spss modeler and a dataset including 533 CPPs, were used for model screening. RESULTS:The results indicated that the machine learning model of Support Vector Machine (SVM) was suitable for predicting membrane penetrating capability. For improvement, the three CPPs with the most longer lengths were used to predict CPPs. The penetration capability can be predicted with an accuracy of close to 95%. CONCLUSION:All the results indicated that by using amino acid position as a variable can be a perspective method for predicting CPPs membrane penetrating capability.
journal_name
Curr Comput Aided Drug Desjournal_title
Current computer-aided drug designauthors
Tang J,Ning J,Liu X,Wu B,Hu Rdoi
10.2174/1573409914666180925100355subject
Has Abstractpub_date
2019-01-01 00:00:00pages
206-211issue
3eissn
1573-4099issn
1875-6697pii
CAD-EPUB-93201journal_volume
15pub_type
杂志文章abstract::Taking into consideration the high importance of the drug target 5-α-reductase (5αR) in prostate cancer in this work we are going first to review previous works and discuss works related to the computer aided drug design of 5αR inhibitors. We report new results in the in silico screening of natural 5αR inhibitors. Tra...
journal_title:Current computer-aided drug design
pub_type: 杂志文章,评审
doi:10.2174/157340911798260368
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journal_title:Current computer-aided drug design
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journal_title:Current computer-aided drug design
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journal_title:Current computer-aided drug design
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journal_title:Current computer-aided drug design
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journal_title:Current computer-aided drug design
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journal_title:Current computer-aided drug design
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journal_title:Current computer-aided drug design
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journal_title:Current computer-aided drug design
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journal_title:Current computer-aided drug design
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journal_title:Current computer-aided drug design
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journal_title:Current computer-aided drug design
pub_type: 杂志文章
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journal_title:Current computer-aided drug design
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journal_title:Current computer-aided drug design
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journal_title:Current computer-aided drug design
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journal_title:Current computer-aided drug design
pub_type: 杂志文章
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journal_title:Current computer-aided drug design
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journal_title:Current computer-aided drug design
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journal_title:Current computer-aided drug design
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journal_title:Current computer-aided drug design
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更新日期:2017-11-10 00:00:00
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journal_title:Current computer-aided drug design
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journal_title:Current computer-aided drug design
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abstract:BACKGROUND:Inhibition of penicillin binding protein 2A (PBP2A) represents a sound drug design strategy in combatting Methicillin resistant Staphylococcus aureus (MRSA). Considering the urgent need for effective antimicrobials in combatting MRSA infections, we have developed a statistically robust ensemble of molecular ...
journal_title:Current computer-aided drug design
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abstract::The role of molecular topology (MT) in the design and selection of new drugs is discussed. After an overview of the different in silico molecular design current technologies, the QSAR analysis is dealt in detail with particular emphasis in the use of topological indices as molecular descriptors. The results of the app...
journal_title:Current computer-aided drug design
pub_type: 杂志文章,评审
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journal_title:Current computer-aided drug design
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更新日期:2020-01-01 00:00:00
abstract:BACKGROUND:The accuracy of molecular conformation for Quantitative Structure Activity Relationship (QSAR) studies is an important criteria, and the most favourable bioactive conformer selection is a tough task. Correct ligand alignment as input for 3D-QSAR is an important step that is prone to human biases. Multiple-di...
journal_title:Current computer-aided drug design
pub_type: 杂志文章
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abstract::As crucial members of the G-protein coupled receptor (GPCR) superfamily, alpha (1)-adrenergic receptors (alpha(1)-ARs) are recognized to intervene the actions of endogenous catecholamines such as norepinephrine and epinephrine. So far three distinct alpha(1)-AR subtypes, alpha(1A), alpha(1B) and alpha(1D), have been c...
journal_title:Current computer-aided drug design
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