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Article ; Online: Chemometric Modelling of Heat Release Capacity, Total Heat Release and Char Formation of Polymers to Assess Their Flammability Characteristics.

Khan, Pathan Mohsin / Roy, Kunal

2020 Volume 41, Issue 1, Page(s) e2000030

Abstract: The quantitative structure-property relationship (QSPR) approach has widely been used to predict several physicochemical properties of materials employing the information obtained from their chemical structures (numerical descriptors). In the present ... ...

Abstract	The quantitative structure-property relationship (QSPR) approach has widely been used to predict several physicochemical properties of materials employing the information obtained from their chemical structures (numerical descriptors). In the present work, we have generated three individual QSPR models for three different endpoints for a large number of polymers in order to determine their fire retardant property such as heat release capacity, total heat release, and %Char, using the only two-dimensional descriptors with definite physicochemical meaning. Relevant subsets of descriptors were selected employing a genetic algorithm approach; subsequently, the selected descriptors were utilised for the identification of the best combination of the variables for the model generation, while the final models were developed employing the partial least squares (PLS) regression algorithm. The generated models were rigorously validated using various internationally accepted internal and external validation metrics. All the models showed promising statistical quality in terms of determination coefficient
MeSH term(s)	Chemometrics ; Hot Temperature ; Least-Squares Analysis ; Polymers/chemistry ; Quantitative Structure-Activity Relationship
Chemical Substances	Polymers
Language	English
Publishing date	2020-05-28
Publishing country	Germany
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2537668-8
ISSN	1868-1751 ; 1868-1743
ISSN (online)	1868-1751
ISSN	1868-1743
DOI	10.1002/minf.202000030
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: In Silico

Khan, Pathan Mohsin / Kumar, Vinay / Roy, Kunal

Combinatorial chemistry & high throughput screening

2020 Volume 24, Issue 8, Page(s) 1281–1299

Abstract: Background: The quantitative structure-activity relationship (QSAR) approach is most widely used for the prediction of biological activity of potential medicinal compounds. A QSAR model is developed by correlating the information obtained from chemical ... ...

Abstract	Background: The quantitative structure-activity relationship (QSAR) approach is most widely used for the prediction of biological activity of potential medicinal compounds. A QSAR model is developed by correlating the information obtained from chemical structures (numerical descriptors/ independent variables) with the experimental response values (the dependent variable). Methods: In the current study, we have developed a QSAR model to predict the inhibitory activity of small molecule carboxamides against severe acute respiratory syndrome coronavirus (SARS-- CoV) 3CLpro enzyme. Due to the structural similarity of this enzyme with SARS-CoV-2, the causative organism of the recent pandemic, the former may be used for the development of therapies against coronavirus disease 19 (COVID-19). Results: The final multiple linear regression (MLR) model was based on four two-dimensional descriptors with definite physicochemical meaning. The model was strictly validated using different internal and external quality metrics. The model showed significant statistical quality in terms of determination coefficient (R2=0.748, adjusted R2 or R2 Conclusion: The derived model may be useful to predict the inhibitory activity of small molecules within the applicability domain of the model only based on the chemical structure information prior to their synthesis and testing.
MeSH term(s)	COVID-19 ; Computer Simulation ; Humans ; Molecular Docking Simulation ; Peptide Hydrolases ; Protease Inhibitors/pharmacology ; Quantitative Structure-Activity Relationship ; SARS-CoV-2
Chemical Substances	Protease Inhibitors ; Peptide Hydrolases (EC 3.4.-)
Keywords	covid19
Language	English
Publishing date	2020-09-15
Publishing country	United Arab Emirates
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2064785-2
ISSN	1875-5402 ; 1386-2073
ISSN (online)	1875-5402
ISSN	1386-2073
DOI	10.2174/1386207323666200914094712
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Article ; Online: Current approaches for choosing feature selection and learning algorithms in quantitative structure-activity relationships (QSAR).

Khan, Pathan Mohsin / Roy, Kunal

Expert opinion on drug discovery

2018 Volume 13, Issue 12, Page(s) 1075–1089

Abstract: Introduction: Quantitative structure-activity/property relationships (QSAR/QSPR) are statistical models which quantitatively correlate quantitative chemical structure information (described as molecular descriptors) to the response end points ( ... ...

Abstract	Introduction: Quantitative structure-activity/property relationships (QSAR/QSPR) are statistical models which quantitatively correlate quantitative chemical structure information (described as molecular descriptors) to the response end points (biological activity, property, toxicity, etc.). Important strategies for QSAR model development and validation include dataset curation, variable selection, and dataset division, selection of modeling algorithms and appropriate measures of model validation. Areas covered: Different feature selection methods and various linear and nonlinear learning algorithms are employed to address the complexity of data sets for selection of appropriate features important for the responses being modeled, to reduce overfitting of the models, and to derive interpretable models. This review provides an overview of various feature selection methods as well as different statistical learning algorithms for QSAR modeling at an elementary level for nonexpert readers. Expert opinion: Novel sets of descriptors are being continuously introduced to this field; therefore, to handle this issue, there is a need to improve new tools for feature selection, which can lead to development of statistically meaningful models, usable by nonexperts in the fields. While handling data sets of limited size, special techniques like double cross-validation and consensus modeling might be more meaningful in order to remove the possibility of bias in descriptor selection.
MeSH term(s)	Algorithms ; Bias ; Drug Design ; Humans ; Models, Molecular ; Models, Statistical ; Pharmaceutical Preparations/administration & dosage ; Pharmaceutical Preparations/chemistry ; Quantitative Structure-Activity Relationship
Chemical Substances	Pharmaceutical Preparations
Language	English
Publishing date	2018-11-03
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2259618-5
ISSN	1746-045X ; 1746-0441
ISSN (online)	1746-045X
ISSN	1746-0441
DOI	10.1080/17460441.2018.1542428
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Article ; Online: Examining the Effects of Normal Ageing on Cortical Connectivity of Older Adults.

Panhwar, Muhammad Aamir / Pathan, Muhammad Mohsin / Pirzada, Nasrullah / Abbasi, Muhammad Aashed Khan / ZhongLiang, Deng / Panhwar, Ghazala

Brain topography

2022 Volume 35, Issue 4, Page(s) 507–524

Abstract: ... networks and a alike least path length, indicative of that they were "small world". To analyze the effect ...

Abstract	With the recent advancement in computer technology, we can extract the picture of the brain as a network. The aim of this study is to constructs large scale individual anatomical brain networks using regional gray matter cortical thickness from individual subject's magnetic resonance imaging (MRI) data, as well as to investigate changes with normal aging in global network organization. The dataset includes 183 healthy subjects sMRI data with an age range from 50 to 80 plus. For all brain networks, we calculated the global network measures and nodal network measures by using network analysis toolkit GRETNA. From global network measurements we calculated small-world measurements and network efficiency measurements, from nodal measurements we calculated node clustering coefficient (CC) and node efficiency at a wide-range of threshold values. All small world measurements showed more clustering at all the given threshold values than random networks and a alike least path length, indicative of that they were "small world". To analyze the effect normal ageing on networks organization, the networks of subjects were categorized into three age groups (50s, 60s, and 70 over). The global and nodal network measurements of each group were statistically analyzed to investigate the significant difference in network organization with in age groups. Results shows that the age has no significance effect in global measurements of brain network. However, by analysis the nodal measures of brain network between age group, network nodes from brain frontal lobe and temporal lobe showed age related significant difference. The results obtained from the proposed study suggest that this network method can deliver a concise network-level picture of brain organization and be used from the outlook of composite networks to investigate inter-individual variability in brain morphology.
MeSH term(s)	Aged ; Aging ; Brain/diagnostic imaging ; Brain/pathology ; Cluster Analysis ; Humans ; Magnetic Resonance Imaging/methods ; Nerve Net/diagnostic imaging
Language	English
Publishing date	2022-01-24
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1078442-1
ISSN	1573-6792 ; 0896-0267
ISSN (online)	1573-6792
ISSN	0896-0267
DOI	10.1007/s10548-021-00884-8
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Article ; Online: QSPR Modeling of the Refractive Index for Diverse Polymers Using 2D Descriptors

Pathan Mohsin Khan / Bakhtiyor Rasulev / Kunal Roy

ACS Omega, Vol 3, Iss 10, Pp 13374-

2018 Volume 13386

Keywords	Chemistry ; QD1-999
Language	English
Publishing date	2018-10-01T00:00:00Z
Publisher	American Chemical Society
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Chemometric modeling of Daphnia magna toxicity of agrochemicals.

Khan, Pathan Mohsin / Roy, Kunal / Benfenati, Emilio

Chemosphere

2019 Volume 224, Page(s) 470–479

Abstract: Over the past few years, the ecotoxicological hazard potential of agrochemicals has received much attention in the industries and regulatory agencies. In the current work, we have developed quantitative structure-activity relationship (QSAR) models for ... ...

Abstract	Over the past few years, the ecotoxicological hazard potential of agrochemicals has received much attention in the industries and regulatory agencies. In the current work, we have developed quantitative structure-activity relationship (QSAR) models for Daphnia magna toxicities of different classes of agrochemicals (fungicides, herbicides, insecticides and microbiocides) individually as well as for the combined set with the application of Organization for Economic Co-operation and Development (OECD) recommended guidelines. The models for the individual data sets as well as for the combined set were generated employing only simple and interpretable two-dimensional descriptors, and subsequently strictly validated using test set compounds. The validated individual models were used to generate consensus models, with the objective to improve the prediction quality and reduced prediction errors. All the individual models of different classes of agrochemicals as well as the global set of agrochemicals showed encouraging statistical quality and prediction ability. The general observations from the derived models suggest that the toxicity increases with lipophilicity and decreases with polarity. The generated models of different classes of agrochemicals and also for the combined set should be applicable for data gap filling for new or untested agrochemical compounds falling within the applicability domain of the developed models.
MeSH term(s)	Agrochemicals/toxicity ; Animals ; Daphnia/drug effects ; Ecotoxicology ; Fungicides, Industrial/toxicity ; Herbicides/toxicity ; Insecticides/toxicity ; Models, Biological ; Models, Chemical ; Quantitative Structure-Activity Relationship ; Toxicity Tests, Acute/methods ; Water Pollutants, Chemical/toxicity
Chemical Substances	Agrochemicals ; Fungicides, Industrial ; Herbicides ; Insecticides ; Water Pollutants, Chemical
Language	English
Publishing date	2019-02-25
Publishing country	England
Document type	Journal Article
ZDB-ID	120089-6
ISSN	1879-1298 ; 0045-6535 ; 0366-7111
ISSN (online)	1879-1298
ISSN	0045-6535 ; 0366-7111
DOI	10.1016/j.chemosphere.2019.02.147
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Article ; Online: First report on chemometric modeling of hydrolysis half-lives of organic chemicals.

Khan, Pathan Mohsin / Lombardo, Anna / Benfenati, Emilio / Roy, Kunal

Environmental science and pollution research international

2020 Volume 28, Issue 2, Page(s) 1627–1642

Abstract: Hydrolysis is one of the most important processes of transformation of organic chemicals in water. The rates of reactions, final chemical entities of these processes, and half-lives of organic chemicals are of considerable interest to environmental ... ...

Abstract	Hydrolysis is one of the most important processes of transformation of organic chemicals in water. The rates of reactions, final chemical entities of these processes, and half-lives of organic chemicals are of considerable interest to environmental chemists as well as authorities involved in the controlling the processing and disposal of such organic chemicals. In this study, we have proposed QSPR models for the prediction of hydrolysis half-life of organic chemicals as a function of different pH and temperature conditions using only two-dimensional molecular descriptors with definite physicochemical significance. For each model, suitable subsets of variables were elected using a genetic algorithm method; next, the elected subsets of variables were subjected to the best subset selection with a key objective to determine the best combination of descriptors for model generation. Finally, QSPR models were constructed using the best combination of variables employing the partial least squares (PLS) regression technique. Next, every final model was subjected for strict validation employing the internationally accepted internal and external validation parameters. The proposed models could be applicable for data gap filling to determine hydrolysis half-lives of organic chemicals at different environmental conditions. Generally, presence of aliphatic ether and ether functional groups, high percentage of oxygen content in the molecule and presence of O-Si pairs of atoms at topological distance one, results in a shorter hydrolysis half-life of organic chemicals. On the other hand, higher unsaturation content and high percentage of nitrogen content in molecules lead to higher hydrolysis half-life. It is also found that branched and compact molecules will have a lower half-life while straight chain analogues will have a higher half-life. To the best of our knowledge, the presented models are the first reported QSPR models for hydrolysis half-lives of organic chemicals at different pH values.
MeSH term(s)	Half-Life ; Hydrolysis ; Least-Squares Analysis ; Organic Chemicals ; Quantitative Structure-Activity Relationship
Chemical Substances	Organic Chemicals
Language	English
Publishing date	2020-08-26
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-020-10500-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: First report on chemometric modeling of hydrolysis half-lives of organic chemicals

Khan, Pathan Mohsin / Lombardo, Anna / Benfenati, Emilio / Roy, Kunal

Environ Sci Pollut Res. 2021 Jan., v. 28, no. 2 p.1627-1642

2021

Abstract	Hydrolysis is one of the most important processes of transformation of organic chemicals in water. The rates of reactions, final chemical entities of these processes, and half-lives of organic chemicals are of considerable interest to environmental chemists as well as authorities involved in the controlling the processing and disposal of such organic chemicals. In this study, we have proposed QSPR models for the prediction of hydrolysis half-life of organic chemicals as a function of different pH and temperature conditions using only two-dimensional molecular descriptors with definite physicochemical significance. For each model, suitable subsets of variables were elected using a genetic algorithm method; next, the elected subsets of variables were subjected to the best subset selection with a key objective to determine the best combination of descriptors for model generation. Finally, QSPR models were constructed using the best combination of variables employing the partial least squares (PLS) regression technique. Next, every final model was subjected for strict validation employing the internationally accepted internal and external validation parameters. The proposed models could be applicable for data gap filling to determine hydrolysis half-lives of organic chemicals at different environmental conditions. Generally, presence of aliphatic ether and ether functional groups, high percentage of oxygen content in the molecule and presence of O–Si pairs of atoms at topological distance one, results in a shorter hydrolysis half-life of organic chemicals. On the other hand, higher unsaturation content and high percentage of nitrogen content in molecules lead to higher hydrolysis half-life. It is also found that branched and compact molecules will have a lower half-life while straight chain analogues will have a higher half-life. To the best of our knowledge, the presented models are the first reported QSPR models for hydrolysis half-lives of organic chemicals at different pH values.
Keywords	algorithms ; chemometrics ; half life ; hydrolysis ; models ; nitrogen content ; oxygen ; pH ; prediction ; temperature ; topology
Language	English
Dates of publication	2021-01
Size	p. 1627-1642.
Publishing place	Springer Berlin Heidelberg
Document type	Article ; Online
Note	NAL-AP-2-clean
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-020-10500-0
Database	NAL-Catalogue (AGRICOLA)

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Article: Chemometric modeling of Daphnia magna toxicity of agrochemicals

Khan, Pathan Mohsin / Kunal Roy / Emilio Benfenati

Chemosphere. 2019 June, v. 224

2019

Abstract	Over the past few years, the ecotoxicological hazard potential of agrochemicals has received much attention in the industries and regulatory agencies. In the current work, we have developed quantitative structure-activity relationship (QSAR) models for Daphnia magna toxicities of different classes of agrochemicals (fungicides, herbicides, insecticides and microbiocides) individually as well as for the combined set with the application of Organization for Economic Co-operation and Development (OECD) recommended guidelines. The models for the individual data sets as well as for the combined set were generated employing only simple and interpretable two-dimensional descriptors, and subsequently strictly validated using test set compounds. The validated individual models were used to generate consensus models, with the objective to improve the prediction quality and reduced prediction errors. All the individual models of different classes of agrochemicals as well as the global set of agrochemicals showed encouraging statistical quality and prediction ability. The general observations from the derived models suggest that the toxicity increases with lipophilicity and decreases with polarity. The generated models of different classes of agrochemicals and also for the combined set should be applicable for data gap filling for new or untested agrochemical compounds falling within the applicability domain of the developed models.
Keywords	Daphnia magna ; agrochemicals ; anti-infective agents ; chemometrics ; data collection ; ecotoxicology ; fungicides ; guidelines ; herbicides ; industry ; insecticides ; lipophilicity ; models ; prediction ; quantitative structure-activity relationships ; toxicity
Language	English
Dates of publication	2019-06
Size	p. 470-479.
Publishing place	Elsevier Ltd
Document type	Article
ZDB-ID	120089-6
ISSN	1879-1298 ; 0045-6535 ; 0366-7111
ISSN (online)	1879-1298
ISSN	0045-6535 ; 0366-7111
DOI	10.1016/j.chemosphere.2019.02.147
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: QSPR Modeling of the Refractive Index for Diverse Polymers Using 2D Descriptors.

Khan, Pathan Mohsin / Rasulev, Bakhtiyor / Roy, Kunal

ACS omega

2018 Volume 3, Issue 10, Page(s) 13374–13386

Abstract: In the present work, predictive quantitative structure-property relationship models have been developed to predict refractive indices (RIs) of a set of 221 diverse organic polymers using theoretical two-dimensional descriptors generated on the basis of ... ...

Abstract	In the present work, predictive quantitative structure-property relationship models have been developed to predict refractive indices (RIs) of a set of 221 diverse organic polymers using theoretical two-dimensional descriptors generated on the basis of the structures of polymers' monomer units. Four models have been developed by applying partial least squares (PLS) regression with a different combination of six descriptors obtained via double cross-validation approaches. The predictive ability and robustness of the proposed models were checked using multiple validation strategies. Subsequently, the validated models were used for the generation of "intelligent" consensus models (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) to improve the quality of predictions for the external data set. The selected consensus models were used for the prediction of refractive index values of various classes of polymers. The final selected model was used to predict the refractive index of four small virtual libraries of monomers recently reported. We also used a true external data set of 98 diverse monomer units with the experimental RI values of the corresponding polymers. The obtained models showed a good predictive ability as evidenced from a very good external predicted variance.
Language	English
Publishing date	2018-10-17
Publishing country	United States
Document type	Journal Article
ISSN	2470-1343
ISSN (online)	2470-1343
DOI	10.1021/acsomega.8b01834
Database	MEDical Literature Analysis and Retrieval System OnLINE

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