A Stacking-Based Classification Approach: Case Study in Volatility Prediction of HIV-1

2022 ◽  
pp. 355-365
Author(s):  
Mohammad Fili ◽  
Guiping Hu ◽  
Changze Han ◽  
Alexa Kort ◽  
Hillel Haim
Keyword(s):  
Classification Approach ◽  
Hiv 1

Novel radial distribution function approach in the study of point mutations: the HIV-1 protease case study

2020 ◽  
Author(s):  
Jurica Novak ◽  
Maria A Grishina ◽  
Vladimir A Potemkin
Keyword(s):  
Distribution Function ◽  
Radial Distribution Function ◽  
Radial Distribution ◽  
Point Mutations ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Inhibition Constant ◽  
The One ◽  
Hiv 1

Background: Mutations are one of the engines of evolution. Under constant stress pressure, mutations can lead to the emergence of unwanted, drug resistant entities. Methodology: The radial distribution function weighted by the number of valence shell electrons is used to design quantitative structure–activity relationship (QSAR) model relating descriptors with the inhibition constant for a series of wild-type HIV-1 protease inhibitor complexes. The residuals of complexes with mutant HIV-1 protease were correlated with the energy of the highest occupied molecular orbitals of the residues introduced to enzyme via point mutations. Conclusion: Successful identification of residues Ile3, Asp25, Val32 and Ile50 as the one whose substitution influences the inhibition constant the most, demonstrates the potential of the proposed methodology for the study of the effects of point mutations.

scholarly journals B-cell–lineage immunogen design in vaccine development with HIV-1 as a case study

2012 ◽  
Vol 30 (5) ◽  
pp. 423-433 ◽  
Author(s):  
Barton F Haynes ◽  
Garnett Kelsoe ◽  
Stephen C Harrison ◽  
Thomas B Kepler
Keyword(s):  
B Cell ◽  
Vaccine Development ◽  
Cell Lineage ◽  
Immunogen Design ◽  
Hiv 1

Classification as Evaluation

2011 ◽  
pp. 41-62 ◽  
Author(s):  
Sari Hakkarainen ◽  
Darijus Strasunskas ◽  
Lillian Hella ◽  
Stine Tuxen
Keyword(s):  
Web Applications ◽  
Evaluation Criteria ◽  
Web Based ◽  
Classification Approach ◽  
The Core ◽  
Ontology Language ◽  
Ontology Building ◽  
Using Data

Ontology is the core component in Semantic Web applications. The employment of an ontology building method affects the quality of ontology and the applicability of ontology language. A weighted classification approach for ontology building guidelines is presented in this chapter. The evaluation criteria are based on an existing classification scheme of a semiotic framework for evaluating the quality of conceptual models. A sample of Web-based ontology building method guidelines is evaluated in general and experimented with using data from a case study in particular. Directions for further refinement of ontology building methods are discussed.

Free Energy Component Analysis for Drug Design:  A Case Study of HIV-1 Protease−Inhibitor Binding

2001 ◽  
Vol 44 (25) ◽  
pp. 4325-4338 ◽  
Author(s):  
Parul Kalra ◽  
T. Vasisht Reddy ◽  
B. Jayaram
Keyword(s):  
Free Energy ◽  
Drug Design ◽  
Protease Inhibitor ◽  
Component Analysis ◽  
Inhibitor Binding ◽  
Energy Component ◽  
Hiv 1

Case Study: Behavioral Symptoms of Pediatric HIV-1 Encephalopathy Successfully Treated with Clonidine

1995 ◽  
Vol 34 (3) ◽  
pp. 302-306 ◽  
Author(s):  
Martha Ceseña ◽  
Douglas O. Lee ◽  
Ana M. Cebollero ◽  
Ronald J. Steingard
Keyword(s):  
Pediatric Hiv ◽  
Behavioral Symptoms ◽  
Hiv 1

scholarly journals An automated workflow by using KNIME Analytical Platform: a case study for modelling and predicting HIV-1 protease inhibitors

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Ramtin Ranji ◽  
Chanat Thanavanich ◽  
Sri Devi Sukumaran ◽  
Sila Kittiwachana ◽  
Sharifuddin Md Zain ◽  
...  
Keyword(s):  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Optimal Number ◽  
Time Cost ◽  
Biological Behaviour ◽  
Structure Activity ◽  
Target Disease ◽  
Drugbank Database ◽  
Hiv 1

In this study, we have demonstrated an automated workflow by using KNIME Analytical Platform for modelling and predicting potential HIV-1 protease (HIVP) inhibitors. The workflow has been simplified in three easy steps i.e., 1) retrievethe database of inhibitors for the target disease from ChEMBL website and well-known drug from DrugBank database, 2) generate the descriptors and, 3) select the optimal number of features after machine learning models training. Our results have indicated that the random forest with auto prediction validation method is the most reliable with the best R2 value of 0.9394. Apparently, this workflow can be transformed easily for any other diseases and the quantitative structure-activity relationship (QSAR) model that has been developed can accurately predict in silico how chemical modifications might influence biological behaviour. Overall, the automated workflow which has been presented in this study may significantly reduce the time, cost and efforts needed to design or develop potential HIVP inhibitors.

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