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Article ; Online: Gold (I) Complexes of 2,2'-Biimidazolate Ligand

Kunal Roy

Iranian Journal of Chemistry & Chemical Engineering, Vol 38, Iss 1, Pp 73-

Syntheses and Spectral Properties of Hetero-polymetallic Complexes Having Ru2Au2 Core

2019 Volume 82

Abstract: The reaction of [Ru(L)2(H2biim)](ClO4)2 [L1 = 2,2¢-bipyridine (bpy) or L2 = 2-(phenylazo)pyridine (pap) and H2biim = 2,2¢-biimidazole] with Au(tht)Cl (tht = tetrahydrothiophene) in presence of base in methanol produced a terametallic cationic complex [{ ... ...

Abstract	The reaction of [Ru(L)2(H2biim)](ClO4)2 [L1 = 2,2¢-bipyridine (bpy) or L2 = 2-(phenylazo)pyridine (pap) and H2biim = 2,2¢-biimidazole] with Au(tht)Cl (tht = tetrahydrothiophene) in presence of base in methanol produced a terametallic cationic complex [{Ru(L)2(biim)}2Au2]2+, [1]2+ which was isolated as its perchlorate salt. The compounds were characterized by various spectroscopic techniques. ESIMS data of these fully corroborate with their formulation. Spectral data of all the polymetallic systems are reported and compared. The redox properties of the complexes are interesting. The complex [{Ru(bpy)2(biim)}2Au2](ClO4)2 showed one irreversible metal based oxidation at 1.43 V whereas the compound [{Ru(pap)2(biim)}2Au2](ClO4)2 showed one irreversible ligand based oxidation at 1.34 V. This type of binding mode of the biimidazolate anion containing Ru2Au2 core is rare in the literature. ‘Metal complex as ligand’ strategy provides a platform for the synthesis novel polymetallic complexes of gold(I).
Keywords	biimidazole ligand ; gold(i) complexes ; hetero-polymetallic complex ; self-assembly ; Chemical engineering ; TP155-156 ; Chemistry ; QD1-999
Subject code	540
Language	English
Publishing date	2019-02-01T00:00:00Z
Publisher	Iranian Institute of Research and Development in Chemical Industries (IRDCI)-ACECR
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Nitroaromatics as hypoxic cell radiosensitizers

Priyanka De / Kunal Roy

European Journal of Medicinal Chemistry Reports, Vol 4, Iss , Pp 100035- (2022)

A 2D-QSAR approach to explore structural features contributing to radiosensitization effectiveness

2022

Abstract: Hypoxia is the prime component of tumor microenvironment that plays a pivotal role in cancer progression. Nitroaromatic compounds are known to enhance the sensitivity of hypoxic cells to ionizing radiation. The application of computational tools like ... ...

Abstract	Hypoxia is the prime component of tumor microenvironment that plays a pivotal role in cancer progression. Nitroaromatic compounds are known to enhance the sensitivity of hypoxic cells to ionizing radiation. The application of computational tools like Quantitative Structure-Activity Relationship (QSAR) can be used to predict newly developed nitroaromatics or compounds with missing data. In the present work, three datasets consisting of 18 nitrofurans, 11 nitrothiophenes and 84 nitroimidazoles were utilised for two-dimensional QSAR modeling to retrieve their structural features essential to elicit radiosensitivity. The work comprises two parts: (i) local modeling using individual datasets; and (ii) global modeling by clubbing the three datasets. The two-dimensional descriptors were calculated using Dragon (version 7.0) software. The developed models were obtained using various feature selection techniques applied in “Small Dataset Modeling” and “Double Cross Validation” tools available from https://dtclab.webs.com/software-tools. Finally, the models were validated using stringent metrics following the Organisation for Economic Co-operation and Development (OECD) guidelines. The developed models are robust, predictive, and are useful tools to predict the radiosensitization of newly developed nitroaromatics. Furthermore, the global model was used to predict two external sets comprising 10 and 47 compounds, and the prediction ability was validated using the “Prediction Reliability Indicator” tool.
Keywords	Hypoxia ; Nitroaromatics ; Radiosensitization ; Radiosentization effectiveness ; QSAR ; Pharmacy and materia medica ; RS1-441 ; Other systems of medicine ; RZ201-999
Subject code	540
Language	English
Publishing date	2022-04-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO2 with periodic table descriptors using machine learning approaches

Joyita Roy / Souvik Pore / Kunal Roy

Beilstein Journal of Nanotechnology, Vol 14, Iss 1, Pp 939-

2023 Volume 950

Abstract: Nanoparticles with their unique features have attracted researchers over the past decades. Heavy metals, upon release and emission, may interact with different environmental components, which may lead to co-exposure to living organisms. Nanoscale ... ...

Abstract	Nanoparticles with their unique features have attracted researchers over the past decades. Heavy metals, upon release and emission, may interact with different environmental components, which may lead to co-exposure to living organisms. Nanoscale titanium dioxide (nano-TiO2) can adsorb heavy metals. The current idea is that nanoparticles (NPs) may act as carriers and facilitate the entry of heavy metals into organisms. Thus, the present study reports nanoscale quantitative structure–activity relationship (nano-QSAR) models, which are based on an ensemble learning approach, for predicting the cytotoxicity of heavy metals adsorbed on nano-TiO2 to human renal cortex proximal tubule epithelial (HK-2) cells. The ensemble learning approach implements gradient boosting and bagging algorithms; that is, random forest, AdaBoost, Gradient Boost, and Extreme Gradient Boost were constructed and utilized to establish statistically significant relationships between the structural properties of NPs and the cause of cytotoxicity. To demonstrate the predictive ability of the developed nano-QSAR models, simple periodic table descriptors requiring low computational resources were utilized. The nano-QSAR models generated good R2 values (0.99–0.89), Q2 values (0.64–0.77), and Q2F1 values (0.99–0.71). Thus, the present work manifests that ML in conjunction with periodic table descriptors can be used to explore the features and predict unknown compounds with similar properties.
Keywords	heavy metals ; hk-2 cell ; ml algorithm ; periodic table descriptors ; qsar ; Technology ; T ; Chemical technology ; TP1-1185 ; Science ; Q ; Physics ; QC1-999
Subject code	540
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	Beilstein-Institut
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Intelligent consensus predictions of bioconcentration factor of pharmaceuticals using 2D and fragment-based descriptors

Kabiruddin Khan / Vinay Kumar / Erika Colombo / Anna Lombardo / Emilio Benfenati / Kunal Roy

Environment International, Vol 170, Iss , Pp 107625- (2022)

2022

Abstract: Bioconcentration factors (BCFs) are markers of chemical substance accumulation in organisms, and they play a significant role in determining the environmental risk of various chemicals. Experiments to obtain BCFs are expensive and time-consuming; ... ...

Abstract	Bioconcentration factors (BCFs) are markers of chemical substance accumulation in organisms, and they play a significant role in determining the environmental risk of various chemicals. Experiments to obtain BCFs are expensive and time-consuming; therefore, it is better to estimate BCF early in the chemical development process. The current research aims to evaluate the ecotoxicity potential of 122 pharmaceuticals and identify possible important structural attributes using BCF as the determining feature against a group of fish species. We have calculated the theoretical 2D descriptors from the OCHEM platform and SiRMS descriptor calculating software. The regression-based quantitative structure–property relationship (QSPR) modeling was used to identify the chemical features responsible for acute fish bioconcentration. Multiple models with the “intelligent consensus” algorithm were employed for the regression-based approach improving the predictive ability of the models. To ensure the robustness and interpretability of the developed models, rigorous validation was performed employing various statistical internal and external validation metrics. From the developed models, it can be specified that the presence of large lipophilic and electronegative moieties greatly enhances the bioaccumulative potential of pharmaceuticals, whereas the hydrophilic characteristics have shown a negative impact on BCF. Furthermore, the developed models were employed to screen the DrugBank database (https://go.drugbank.com/) for assessing the BCF properties of the entire database. The evidence acquired from the modeled descriptors might be used for aquatic risk assessment in the future, with the added benefit of providing an early caution of their probable negative impact on aquatic ecosystems for regulatory purposes.
Keywords	BCF ; DrugBank ; Ecotoxicity ; ECOSAR ; OCHEM ; Pharmaceuticals ; Environmental sciences ; GE1-350
Subject code	660
Language	English
Publishing date	2022-12-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: PLS regression-based chemometric modeling of odorant properties of diverse chemical constituents of black tea and coffee

Ojha, Probir Kumar / Kunal Roy

RSC advances. 2018 Jan. 09, v. 8, no. 5

2018

Abstract: Tea and coffee are the most attractive non-alcoholic beverages used worldwide due to the odorant properties of diverse components present in these beverages. The aim of this work is to investigate the key structural features which regulate the odorant ... ...

Abstract	Tea and coffee are the most attractive non-alcoholic beverages used worldwide due to the odorant properties of diverse components present in these beverages. The aim of this work is to investigate the key structural features which regulate the odorant properties of constituents present in black tea and coffee using regression-based chemometric models. We have also investigated the key structural properties which create the odor difference between tea and coffee. We have employed different variable selection strategies to extract the most relevant variables prior to development of final partial least squares (PLS) models. The models were extensively validated using different validation metrics, and the results justify the reliability and usefulness of the developed predictive PLS models. The best PLS model captured the necessary structural information on relative hydrophobic surface area, heteroatoms with higher number of multiple bonds, hydrogen atoms connected to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp) fragments, electron-richness, C–O atom pairs at the topological distance 10 and surface weighted charged partial negative surface areas for explaining the odorant properties of the constituents present in black tea. On the other hand, C–S atom pairs at the topological distance 1, C–C atom pairs at the topological distance 5, donor atoms like N and O for hydrogen bonds, hydrogen atoms connected to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp) fragments and R–CX–X fragments (where, R represents any group linked through carbon and X represents any heteroatom (O, N, S, P, Se, and halogens)) are the key structural components captured by the PLS model developed from the constituents present in coffee. The developed models can thus be successfully utilized for in silico prediction of odorant properties of diverse classes of compounds and exploration of the structural information which creates the odor difference between black tea and coffee.
Keywords	beverages ; black tea ; carbon ; chemical composition ; chemometrics ; halogens ; hydrogen ; hydrogen bonding ; hydrophobicity ; models ; odor compounds ; odors ; prediction ; selenium ; surface area ; topology
Language	English
Dates of publication	2018-0109
Size	p. 2293-2304.
Publishing place	The Royal Society of Chemistry
Document type	Article
ISSN	2046-2069
DOI	10.1039/c7ra12914a
Database	NAL-Catalogue (AGRICOLA)

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Article: Development of a robust and validated 2D-QSPR model for sweetness potency of diverse functional organic molecules

Ojha, Probir Kumar / Kunal Roy

Food and chemical toxicology. 2018 Feb., v. 112

2018

Abstract: In the present report, we have developed a predictive QSPR model using only easily computable two-dimensional (2D) descriptors from diverse classes of sweetening agents to find out the key structural properties which regulate their sweet potency. The ... ...

Abstract	In the present report, we have developed a predictive QSPR model using only easily computable two-dimensional (2D) descriptors from diverse classes of sweetening agents to find out the key structural properties which regulate their sweet potency. The available data set was curated to remove salts, mixtures and compounds without having a definite structure. A k-fold double cross validation technique was employed for variable selection prior to development of the final model. The final model was developed using partial least squares (PLS) regression analysis and selected based on a mean absolute error (MAE) based criteria for the validation sets. The model was validated extensively using different internal and external validation strategies in accordance with the Organization for Economic Co-operation and Development (OECD) guidelines. This work presented development of a validated quantitative structure-property relationship (QSPR) model obtained from k-fold double cross-validation technique in order to find out the key structural information required to enhance the sweet potency of the molecules. Finally, we have designed and proposed 13 new molecules based on the insights obtained from the QSPR model. The designed compounds showed good in silico predicted sweetness potency with acceptable ADMET profile.
Keywords	chemical elements ; data collection ; guidelines ; least squares ; models ; quantitative structure-activity relationships ; salts ; sweeteners ; sweetness ; toxicology
Language	English
Dates of publication	2018-02
Size	p. 551-562.
Publishing place	Elsevier Ltd
Document type	Article
ZDB-ID	782617-5
ISSN	1873-6351 ; 0278-6915
ISSN (online)	1873-6351
ISSN	0278-6915
DOI	10.1016/j.fct.2017.03.043
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: How Precise Are Our Quantitative Structure–Activity Relationship Derived Predictions for New Query Chemicals?

Kunal Roy / Pravin Ambure / Supratik Kar

ACS Omega, Vol 3, Iss 9, Pp 11392-

2018 Volume 11406

Keywords	Chemistry ; QD1-999
Language	English
Publishing date	2018-09-01T00:00:00Z
Publisher	American Chemical Society
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: 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: Exploring QSPR modeling for adsorption of hazardous synthetic organic chemicals (SOCs) by SWCNTs

Ghosh, Sulekha / Probir Kumar Ojha / Kunal Roy

Chemosphere. 2019 Aug., v. 228

2019

Abstract: In order to understand the physicochemical properties as well as the mechanisms behind adsorption of hazardous synthetic organic chemicals (SOCs) onto single walled carbon nanotubes (SWCNTs), we have developed partial least squares (PLS)-regression based ...

Abstract	In order to understand the physicochemical properties as well as the mechanisms behind adsorption of hazardous synthetic organic chemicals (SOCs) onto single walled carbon nanotubes (SWCNTs), we have developed partial least squares (PLS)-regression based QSPR models using a diverse set of 40 hazardous SOCs having defined adsorption coefficient (logK). The models were extensively validated using different validation parameters in order to assure the robustness and predictivity of the models. We have also checked the consensus predictivity of all the individual models using “Intelligent consensus predictor” tool for possible enhancement of the quality of predictions for test set compounds. The consensus predictivity of the test set compounds were found to be better than the individual models based on not only the MAE based criteria (MAE(95%) = Good) but also some other validation parameters (Q2F1 = 0.938, Q2F2 = 0.937). The contributing descriptors obtained from the QSPR models suggested that the hazardous SOCs may get adsorbed onto the SWCNTs through hydrophobic interaction as well as hydrogen bonding interactions and electrostatic interaction to the functionally modified SWCNTs. Thus, the developed models may provide knowledge to scientists to increase the efficient application of SWCNTs as a special adsorbent, which may be useful for the management of environmental pollution.
Keywords	adsorbents ; adsorption ; carbon nanotubes ; electrostatic interactions ; hydrogen bonding ; hydrophobic bonding ; least squares ; models ; organic compounds ; physicochemical properties ; pollution ; prediction
Language	English
Dates of publication	2019-08
Size	p. 545-555.
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.2019.04.124
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 ... ...

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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