Toward an Optimal Procedure for Variable Selection and QSAR Model Building

2001 ◽  
Vol 41 (5) ◽  
pp. 1218-1227 ◽  
Author(s):  
A. Yasri ◽  
D. Hartsough
Keyword(s):  
Variable Selection ◽  
Model Building ◽  
Qsar Model ◽  
Optimal Procedure

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

scholarly journals Toward an Optimal Procedure for PC-ANN Model Building: Prediction of the Carcinogenic Activity of a Large Set of Drugs.

ChemInform ◽  
2005 ◽  
Vol 36 (16) ◽  
Author(s):  
Bahram Hemmateenejad ◽  
Mohammad A. Safarpour ◽  
Ramin Miri ◽  
Nasim Nesari
Keyword(s):  
Model Building ◽  
Large Set ◽  
Ann Model ◽  
Carcinogenic Activity ◽  
Optimal Procedure

scholarly journals Hipparcos minor planets: Towards an improvement of the model analysis by detecting influence factors

1996 ◽  
Vol 172 ◽  
pp. 447-450 ◽  
Author(s):  
M. L. Bougeard ◽  
J.-F. Bange ◽  
M. Mahfouz ◽  
A. Bec-Borsenberger
Keyword(s):  
Variable Selection ◽  
Statistical Methods ◽  
Preliminary Data ◽  
Reference Frames ◽  
Model Building ◽  
Influence Factors ◽  
Model Analysis ◽  
Short Length ◽  
Minor Planets

In order to evaluate a possible rotation between the Hipparcos and the dynamical reference frames, Hipparcos minor planets preliminary data are analysed. The resolution of the problem is very sensitive to correlations induced by the short length of the interval of observation. Several statistical methods are performed to appreciate the factors of bad conditioning. A procedure for variable selection and model building is given.

Descriptor selection based on variable stability for predicting inhibitor activity

2017 ◽  
Vol 16 (08) ◽  
pp. 1750074
Author(s):  
Jing Chen ◽  
Yunjing Gao ◽  
Xiaoyan Hu ◽  
Dongdong Qin ◽  
Xiaoquan Lu
Keyword(s):  
Standard Deviation ◽  
Variable Selection ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Mean Value ◽  
Exponential Parameter ◽  
Chemical Structures ◽  
Descriptor Selection ◽  
The Difference ◽  
Leave One Out

Quantitative structure-activity relationship (QSAR) has been a technique to study the relationship between chemical structures and properties, and variable selection is an important problem for finding the informative variables and building reliable models. A variable selection method based on variable stability is proposed and used for selecting the informative descriptors in the QSAR model of inhibitors. In the method, a series of models are built by leave-one-out cross validation (LOOCV), and variable stability is defined as the ratio of the absolute mean value and standard deviation of the regression coefficients in the models for a descriptor. Therefore, the descriptors with larger stabilities are more informative to the model. To further enhance the difference among the descriptors, an exponential parameter is used to modify the standard deviation. The results show that 13 descriptors are selected as informative ones from 1217 descriptors for the QSAR model of inhibitors. An effective prediction model can be constructed by them.

Toward an Optimal Procedure for PC-ANN Model Building:  Prediction of the Carcinogenic Activity of a Large Set of Drugs

2005 ◽  
Vol 45 (1) ◽  
pp. 190-199 ◽  
Author(s):  
Bahram Hemmateenejad ◽  
Mohammad A. Safarpour ◽  
Ramin Miri ◽  
Nasim Nesari
Keyword(s):  
Model Building ◽  
Large Set ◽  
Ann Model ◽  
Carcinogenic Activity ◽  
Optimal Procedure

scholarly journals An automated framework for QSAR model building

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Samina Kausar ◽  
Andre O. Falcao
Keyword(s):  
Model Building ◽  
Qsar Model

A Multi-layered Variable Selection Strategy for QSAR Modeling of Butyrylcholinesterase Inhibitors

2020 ◽  
Vol 20 (18) ◽  
pp. 1601-1627 ◽  
Author(s):  
Vinay Kumar ◽  
Priyanka De ◽  
Probir Kumar Ojha ◽  
Achintya Saha ◽  
Kunal Roy
Keyword(s):  
Hydrogen Bond ◽  
Variable Selection ◽  
Inhibitory Activity ◽  
Stepwise Regression ◽  
Qsar Model ◽  
Structural Features ◽  
Selection Strategy ◽  
Pls Regression ◽  
Final Model ◽  
Qsar Models

Background: Alzheimer’s disease (AD), a neurological disorder, is the most common cause of senile dementia. Butyrylcholinesterase (BuChE) enzyme plays a vital role in regulating the brain acetylcholine (ACh) neurotransmitter, but in the case of Alzheimer’s disease (AD), BuChE activity gradually increases in patients with a decrease in the acetylcholine (ACh) concentration via hydrolysis. ACh plays an essential role in regulating learning and memory as the cortex originates from the basal forebrain, and thus, is involved in memory consolidation in these sites. Methods: In this work, we have developed a partial least squares (PLS)-regression based two dimensional quantitative structure-activity relationship (2D-QSAR) model using 1130 diverse chemical classes of compounds with defined activity against the BuChE enzyme. Keeping in mind the strict Organization for Economic Co-operation and Development (OECD) guidelines, we have tried to select significant descriptors from the large initial pool of descriptors using multi-layered variable selection strategy using stepwise regression followed by genetic algorithm (GA) followed by again stepwise regression technique and at the end best subset selection prior to development of final model thus reducing noise in the input. Partial least squares (PLS) regression technique was employed for the development of the final model while model validation was performed using various stringent validation criteria. Results: The results obtained from the QSAR model suggested that the quality of the model is acceptable in terms of both internal (R2= 0.664, Q2= 0.650) and external (R2 Pred= 0.657) validation parameters. The QSAR studies were analyzed, and the structural features (hydrophobic, ring aromatic and hydrogen bond acceptor/donor) responsible for enhancement of the activity were identified. The developed model further suggests that the presence of hydrophobic features like long carbon chain would increase the BuChE inhibitory activity and presence of amino group and hydrazine fragment promoting the hydrogen bond interactions would be important for increasing the inhibitory activity against BuChE enzyme. Conclusion: Furthermore, molecular docking studies have been carried out to understand the molecular interactions between the ligand and receptor, and the results are then correlated with the structural features obtained from the QSAR models. The information obtained from the QSAR models are well corroborated with the results of the docking study.

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