2022 |
|
Supratik Kar, Kavitha Pathakoti, Danuta Leszczynska, Paul B. Tchounwou & Jerzy Leszczynski In vitro and in silico study of mixtures cytotoxicity of metal oxide nanoparticles to Escherichia coli: a mechanistic approach Journal Article Nanotoxicology, 2022, (NSF/CREST HRD-1547754). Abstract | Links | BibTeX | Tags: E. coli, in silico, In vitro, mixtures, Nanoparticles, Toxicity @article{Kar2022, title = {In vitro and in silico study of mixtures cytotoxicity of metal oxide nanoparticles to Escherichia coli: a mechanistic approach}, author = {Supratik Kar, Kavitha Pathakoti, Danuta Leszczynska, Paul B. Tchounwou & Jerzy Leszczynski}, doi = {10.1080/17435390.2022.2123750}, year = {2022}, date = {2022-09-23}, journal = {Nanotoxicology}, abstract = {Metal oxide nanoparticles (MONPs) are commonly found in the aquatic and terrestrial systems as chemical mixtures. Assessment of cytotoxicity associated with single and combination of MONPs can truly identify the concerned environmental risk. Thus, using Escherichia coli as a test model, in vitro cytotoxicity of 6 single MONPs, 15 binary and 20 tertiary mixtures with equitoxic ratios was evaluated following standard bioassay protocols. Assessment of oxidative stress suggested that the production of reactive oxygen species (ROS) was negligible, and the release of metal zinc ions played an important role in the toxicity of MONP mixtures. From our experimental data points, seven quantitative structure-activity relationships (QSARs) models were developed to model the cytotoxicity of these MONPs, based on our created periodic table-based descriptors and experimentally analyzed Zeta-potential. Two strategic approaches i.e. pharmacological and mathematical hypotheses were considered to identify the mixture descriptors pool for modeling purposes. The stringent validation criteria suggested that the model (Model M4) developed with mixture descriptors generated by square-root mole contribution outperformed the other six models considering validation criteria. While considering the pharmacological approach, the ‘independent action’ generated descriptor pool offered the best model (Model M2), which firmly confirmed that each MONP in the mixture acts through ‘independent action’ to induce cytotoxicity to E. colii nstead of fostering an additive, antagonistic or synergistic effect among MONPs. The total metal electronegativity in a specific metal oxide relative to the number of oxygen atoms and metal valence was associated with a positive contribution to cytotoxicity. At the same time, the core count, which gives a measure of molecular bulk and Zeta potential, had a negative contribution to cytotoxicity.}, note = {NSF/CREST HRD-1547754}, keywords = {E. coli, in silico, In vitro, mixtures, Nanoparticles, Toxicity}, pubstate = {published}, tppubtype = {article} } Metal oxide nanoparticles (MONPs) are commonly found in the aquatic and terrestrial systems as chemical mixtures. Assessment of cytotoxicity associated with single and combination of MONPs can truly identify the concerned environmental risk. Thus, using Escherichia coli as a test model, in vitro cytotoxicity of 6 single MONPs, 15 binary and 20 tertiary mixtures with equitoxic ratios was evaluated following standard bioassay protocols. Assessment of oxidative stress suggested that the production of reactive oxygen species (ROS) was negligible, and the release of metal zinc ions played an important role in the toxicity of MONP mixtures. From our experimental data points, seven quantitative structure-activity relationships (QSARs) models were developed to model the cytotoxicity of these MONPs, based on our created periodic table-based descriptors and experimentally analyzed Zeta-potential. Two strategic approaches i.e. pharmacological and mathematical hypotheses were considered to identify the mixture descriptors pool for modeling purposes. The stringent validation criteria suggested that the model (Model M4) developed with mixture descriptors generated by square-root mole contribution outperformed the other six models considering validation criteria. While considering the pharmacological approach, the ‘independent action’ generated descriptor pool offered the best model (Model M2), which firmly confirmed that each MONP in the mixture acts through ‘independent action’ to induce cytotoxicity to E. colii nstead of fostering an additive, antagonistic or synergistic effect among MONPs. The total metal electronegativity in a specific metal oxide relative to the number of oxygen atoms and metal valence was associated with a positive contribution to cytotoxicity. At the same time, the core count, which gives a measure of molecular bulk and Zeta potential, had a negative contribution to cytotoxicity. | |
Liudmyla K. Sviatenko, Leonid Gorb,; Jerzy Leszczynski NTO Degradation by Nitroreductase: A DFT Study Journal Article J. Phys. Chem. B, 126 (32), pp. 5991–6006, 2022, (ARO W911NF-20-1-0116; XSEDE DMR110088 ). Abstract | Links | BibTeX | Tags: NTO @article{Sviatenko2022, title = {NTO Degradation by Nitroreductase: A DFT Study}, author = {Liudmyla K. Sviatenko, Leonid Gorb, and Jerzy Leszczynski}, doi = {10.1021/acs.jpcb.2c04153}, year = {2022}, date = {2022-08-04}, journal = {J. Phys. Chem. B}, volume = {126}, number = {32}, pages = {5991\textendash6006}, abstract = {NTO (5-nitro-1,2,4-triazol-3-one), an energetic material used in military applications, may be released to the environment during manufacturing, transportation, storage, training, and disposal. A detailed investigation of the possible mechanism for all steps of reduction of NTO by oxygen-insensitive nitroreductase, as one of the pathways for NTO environmental degradation, was performed by computational study at the PCM(Pauling)/M06-2X/6-311++G(d,p) level. Obtained results reveal an overall sequence for NTO transformation into ATO (5-amino-1,2,4-triazol-3-one) with the flavin mononucleotide (FMN) cofactor of nitroreductase. Reduction of the nitro group to the nitroso group and the nitroso group to the hydroxylamino group follow a similar mechanism that consists of the sequential electron and proton transfer from the flavin cofactor. The hydride transfer mechanism may contribute to reduction of the nitroso group by the anionic form of the reduced flavin cofactor. Reduction of 5-(hydroxylamino)-1,2,4-triazol-3-one by the neutral form of the reduced flavin is impossible, whereas reduction of the hydroxylamino group to the amino group occurs with the anionic form of the reduced cofactor by a mechanism involving an initial proton transfer from the hydroxonium ion followed by two electrons and one proton transfers from the flavin cofactor. Small activation energies and high exothermicity support the significant contribution of oxygen-insensitive nitroreductase and other enzymes, containing FMN as a cofactor, to NTO degradation in the environment.}, note = {ARO W911NF-20-1-0116; XSEDE DMR110088 }, keywords = {NTO}, pubstate = {published}, tppubtype = {article} } NTO (5-nitro-1,2,4-triazol-3-one), an energetic material used in military applications, may be released to the environment during manufacturing, transportation, storage, training, and disposal. A detailed investigation of the possible mechanism for all steps of reduction of NTO by oxygen-insensitive nitroreductase, as one of the pathways for NTO environmental degradation, was performed by computational study at the PCM(Pauling)/M06-2X/6-311++G(d,p) level. Obtained results reveal an overall sequence for NTO transformation into ATO (5-amino-1,2,4-triazol-3-one) with the flavin mononucleotide (FMN) cofactor of nitroreductase. Reduction of the nitro group to the nitroso group and the nitroso group to the hydroxylamino group follow a similar mechanism that consists of the sequential electron and proton transfer from the flavin cofactor. The hydride transfer mechanism may contribute to reduction of the nitroso group by the anionic form of the reduced flavin cofactor. Reduction of 5-(hydroxylamino)-1,2,4-triazol-3-one by the neutral form of the reduced flavin is impossible, whereas reduction of the hydroxylamino group to the amino group occurs with the anionic form of the reduced cofactor by a mechanism involving an initial proton transfer from the hydroxonium ion followed by two electrons and one proton transfers from the flavin cofactor. Small activation energies and high exothermicity support the significant contribution of oxygen-insensitive nitroreductase and other enzymes, containing FMN as a cofactor, to NTO degradation in the environment. | |
Priyanka De, Vinay Kumar, Supratik Kar, Kunal Roy, Jerzy Leszczynski Struct. Chem. , 33 , pp. 1741–1753, 2022, (NSF/CREST HRD-1547754). Abstract | Links | BibTeX | Tags: COVID-19, In silico approaches, Quantitative structure–activity relationship, Read-across, SARS-CoV-2 @article{De2022, title = {Repurposing FDA approved drugs as possible anti-SARS-CoV-2 medications using ligand-based computational approaches: sum of ranking difference-based model selection}, author = {Priyanka De, Vinay Kumar, Supratik Kar, Kunal Roy, Jerzy Leszczynski}, doi = {10.1007/s11224-022-01975-3}, year = {2022}, date = {2022-06-07}, journal = {Struct. Chem. }, volume = {33}, pages = {1741\textendash1753}, abstract = {The worldwide burden of coronavirus disease 2019 (COVID-19) is still unremittingly prevailing, with more than 440 million infections and over 5.9 million deaths documented so far since the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic. The non-availability of treatment further aggravates the scenario, thereby demanding the exploration of pre-existing FDA-approved drugs for their effectiveness against COVID-19. The current research aims to identify potential anti-SARS-CoV-2 drugs using a computational approach and repurpose them if possible. In the present study, we have collected a set of 44 FDA-approved drugs of different classes from a previously published literature with their potential antiviral activity against COVID-19. We have employed both regression- and classification-based quantitative structure\textendashactivity relationship (QSAR) modeling to identify critical chemical features essential for anticoronaviral activity. Multiple models with the consensus algorithm were employed for the regression-based approach to improve the predictions. Additionally, we have employed a machine learning-based read-across approach using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home and linear discriminant analysis for the efficient prediction of potential drug candidate for COVID-19. Finally, the quantitative prediction ability of different modeling approaches was compared using the sum of ranking differences (SRD). Furthermore, we have predicted a true external set of 98 pharmaceuticals using the developed models for their probable anti-COVID activity and their prediction reliability was checked employing the “Prediction Reliability Indicator” tool available from https://dtclab.webs.com/software-tools. Though the present study does not target any protein of viral interaction, the modeling approaches developed can be helpful for identifying or screening potential anti-coronaviral drug candidates.}, note = {NSF/CREST HRD-1547754}, keywords = {COVID-19, In silico approaches, Quantitative structure\textendashactivity relationship, Read-across, SARS-CoV-2}, pubstate = {published}, tppubtype = {article} } The worldwide burden of coronavirus disease 2019 (COVID-19) is still unremittingly prevailing, with more than 440 million infections and over 5.9 million deaths documented so far since the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic. The non-availability of treatment further aggravates the scenario, thereby demanding the exploration of pre-existing FDA-approved drugs for their effectiveness against COVID-19. The current research aims to identify potential anti-SARS-CoV-2 drugs using a computational approach and repurpose them if possible. In the present study, we have collected a set of 44 FDA-approved drugs of different classes from a previously published literature with their potential antiviral activity against COVID-19. We have employed both regression- and classification-based quantitative structure–activity relationship (QSAR) modeling to identify critical chemical features essential for anticoronaviral activity. Multiple models with the consensus algorithm were employed for the regression-based approach to improve the predictions. Additionally, we have employed a machine learning-based read-across approach using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home and linear discriminant analysis for the efficient prediction of potential drug candidate for COVID-19. Finally, the quantitative prediction ability of different modeling approaches was compared using the sum of ranking differences (SRD). Furthermore, we have predicted a true external set of 98 pharmaceuticals using the developed models for their probable anti-COVID activity and their prediction reliability was checked employing the “Prediction Reliability Indicator” tool available from https://dtclab.webs.com/software-tools. Though the present study does not target any protein of viral interaction, the modeling approaches developed can be helpful for identifying or screening potential anti-coronaviral drug candidates. | |
Leonid Gorb, Mykola Ilchenko, Jerzy Leszczynski Environ. Sci. Pollut. Res., 29 , pp. 68522–68531, 2022, (ARO grant W911NF-20-1-0116 ). Abstract | Links | BibTeX | Tags: Decomposition, Density functional theory, Iron oxide, Nano-cluster, Nanoparticles, NTO, TNT @article{Gorb2022, title = {Decomposition of 2,4,6-trinitrotoluene (TNT) and 5-nitro-2,4- dihydro-3H-1,2,4-triazol-3-one (NTO) by Fe13O13 nanoparticle: density functional theory study}, author = {Leonid Gorb, Mykola Ilchenko, Jerzy Leszczynski}, doi = {10.1007/s11356-022-20547-w}, year = {2022}, date = {2022-05-11}, journal = {Environ. Sci. Pollut. Res.}, volume = {29}, pages = {68522\textendash68531}, abstract = {To obtain more insight into the mechanisms of the decomposition of energetic compounds, we performed a computational study of the interaction of Fe13O13 nanoparticles with two energetic molecules such as 2,4,6-trinitrotoluene (TNT) and 5-nitro-2,4-dihydro-3H-1,2,4-triazol-3-one (NTO). The density functional theory using M06-2X, B3LYP, and BLYP density functionals was applied. We found that the reactivity of these molecules strongly depends on the place of adsorption (so-called top and bottom planes of Fe13O13). Namely, only the interaction with the bottom plane results in the thermodynamic characteristics of the decomposition that provide a medium reaction rate for the studied processes. Several pathways for such decomposition were found. One of them is the inter-complex oxygen transfer of nitro-group oxygen to Fe13O13. This pathway results in the formation of adsorbed nitroso compounds. The second pathway describes a more complex decomposition that includes the transfer of the nitro-group oxygen accompanied by the hydrogen transfer. In all cases, the interaction of energetic molecules with Fe13O13 nanoparticles takes place along with a barrier-less electron transfer from Fe13O13 to TNT or NTO species.}, note = {ARO grant W911NF-20-1-0116 }, keywords = {Decomposition, Density functional theory, Iron oxide, Nano-cluster, Nanoparticles, NTO, TNT}, pubstate = {published}, tppubtype = {article} } To obtain more insight into the mechanisms of the decomposition of energetic compounds, we performed a computational study of the interaction of Fe13O13 nanoparticles with two energetic molecules such as 2,4,6-trinitrotoluene (TNT) and 5-nitro-2,4-dihydro-3H-1,2,4-triazol-3-one (NTO). The density functional theory using M06-2X, B3LYP, and BLYP density functionals was applied. We found that the reactivity of these molecules strongly depends on the place of adsorption (so-called top and bottom planes of Fe13O13). Namely, only the interaction with the bottom plane results in the thermodynamic characteristics of the decomposition that provide a medium reaction rate for the studied processes. Several pathways for such decomposition were found. One of them is the inter-complex oxygen transfer of nitro-group oxygen to Fe13O13. This pathway results in the formation of adsorbed nitroso compounds. The second pathway describes a more complex decomposition that includes the transfer of the nitro-group oxygen accompanied by the hydrogen transfer. In all cases, the interaction of energetic molecules with Fe13O13 nanoparticles takes place along with a barrier-less electron transfer from Fe13O13 to TNT or NTO species. | |
V. Kumar, S. Kar, P. De, K. Roy & J. Leszczynski Identification of potential antivirals against 3CLpro enzyme for the treatment of SARS-CoV-2: A multi-step virtual screening study Journal Article SAR QSAR Environ. Res., 33 (5), pp. 357-386, 2022, (NSF/CREST HRD-1547754). Abstract | Links | BibTeX | Tags: 2D-QSAR, 3CLpro, 3D-QSAR, ADMET docking, SARS CoV-2 @article{Kumar2022, title = {Identification of potential antivirals against 3CLpro enzyme for the treatment of SARS-CoV-2: A multi-step virtual screening study}, author = {V. Kumar, S. Kar, P. De, K. Roy & J. Leszczynski}, doi = {10.1080/1062936X.2022.2055140}, year = {2022}, date = {2022-04-05}, journal = {SAR QSAR Environ. Res.}, volume = {33}, number = {5}, pages = {357-386}, abstract = {The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak is posing a serious public health threat worldwide in the form of COVD-19. Herein, we have performed two-dimensional quantitative structure\textendashactivity relationship (2D-QSAR) and three-dimensional pharmacophore modelling analysis employing inhibitors of 3-chymotrypsin-like protease (3CLpro), the leading protease that is crucial for the replication of SARS-CoV-2. The investigation aims to identify the important structural features responsible for the enzyme inhibition and the search for novel 3CLpro enzyme inhibitors as effective therapeutics for treating SARS-CoV-2. Furthermore, we carried out molecular docking studies using the most and least active compounds in the dataset, aiming to validate the contributions of various features as appeared in the QSAR models. Later, the stringently validated 2D-QSAR model was used to estimate the 3CLpro inhibitory activity of compounds from five chemical databases. Compounds with the significant predicted activity were then subjected to pharmacophore-based virtual screening to screen the top-rated compounds, which were then further subjected to molecular docking analysis, absorption, distribution, metabolism, excretion \textendash toxicity (ADMET) profiling, and molecular dynamics (MD) simulation. The multi-step virtual screening analyses suggested that compounds CASAntiV-865453-58-3, CASAntiV-865453-40-3, and CASAntiV-2043031-84-9 could be used as effective therapeutic agents for the treatment of SARS-CoV-2.}, note = {NSF/CREST HRD-1547754}, keywords = {2D-QSAR, 3CLpro, 3D-QSAR, ADMET docking, SARS CoV-2}, pubstate = {published}, tppubtype = {article} } The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak is posing a serious public health threat worldwide in the form of COVD-19. Herein, we have performed two-dimensional quantitative structure–activity relationship (2D-QSAR) and three-dimensional pharmacophore modelling analysis employing inhibitors of 3-chymotrypsin-like protease (3CLpro), the leading protease that is crucial for the replication of SARS-CoV-2. The investigation aims to identify the important structural features responsible for the enzyme inhibition and the search for novel 3CLpro enzyme inhibitors as effective therapeutics for treating SARS-CoV-2. Furthermore, we carried out molecular docking studies using the most and least active compounds in the dataset, aiming to validate the contributions of various features as appeared in the QSAR models. Later, the stringently validated 2D-QSAR model was used to estimate the 3CLpro inhibitory activity of compounds from five chemical databases. Compounds with the significant predicted activity were then subjected to pharmacophore-based virtual screening to screen the top-rated compounds, which were then further subjected to molecular docking analysis, absorption, distribution, metabolism, excretion – toxicity (ADMET) profiling, and molecular dynamics (MD) simulation. The multi-step virtual screening analyses suggested that compounds CASAntiV-865453-58-3, CASAntiV-865453-40-3, and CASAntiV-2043031-84-9 could be used as effective therapeutic agents for the treatment of SARS-CoV-2. | |
Leonid Gorb, Mykola Ilchenko & Jerzy Leszczynski Molecular Physics, 2022, (ERDC W912HZ-20-20069 NSF-PREM grant no. DMR-1826886 ). Abstract | Links | BibTeX | Tags: adsorption, fluorinated graphene oxide, graphene oxide, Perfluorooctanoic acid, perfluorooctansulphonic acid @article{Gorb2022b, title = {A density functional theory study of the simplest adsorption forms of perfluorooctanoic and perfluorooctanesulphonic acids by graphene oxide and fluorinated graphene oxide.}, author = {Leonid Gorb, Mykola Ilchenko & Jerzy Leszczynski}, doi = {10.1080/00268976.2022.2053218}, year = {2022}, date = {2022-03-19}, journal = {Molecular Physics}, abstract = {A density functional theory augmented by the long-range corrected hybrid density functional ωB97XD and 6-31G(d,p) basis set has been applied to generate the simplest adsorption structure models of perfluorooctanoic and perfluorooctanesulphonic acids adsorbed from a water solution by the surfaces of graphene oxide and fluorinated graphene oxide. It has been revealed that both hydrophilic and hydrophobic sites can adsorb the anions of the investigated acids. The results of our calculations suggest preference in the adsorption ability of graphene oxide compared to its fluorinated counterpart.}, note = {ERDC W912HZ-20-20069 NSF-PREM grant no. DMR-1826886 }, keywords = {adsorption, fluorinated graphene oxide, graphene oxide, Perfluorooctanoic acid, perfluorooctansulphonic acid}, pubstate = {published}, tppubtype = {article} } A density functional theory augmented by the long-range corrected hybrid density functional ωB97XD and 6-31G(d,p) basis set has been applied to generate the simplest adsorption structure models of perfluorooctanoic and perfluorooctanesulphonic acids adsorbed from a water solution by the surfaces of graphene oxide and fluorinated graphene oxide. It has been revealed that both hydrophilic and hydrophobic sites can adsorb the anions of the investigated acids. The results of our calculations suggest preference in the adsorption ability of graphene oxide compared to its fluorinated counterpart. | |
2021 |
|
Jing Wang, Jiande Gu, Asif Rony, Maohong Fan,; Jerzy Leszczynski Theoretical DFT Study on the Mechanisms of CO/CO2 Conversion in Chemical Looping Catalyzed by Calcium Ferrite Journal Article J. Phys. Chem. A , 125 (37), pp. 8159–8167, 2021. Abstract | Links | BibTeX | Tags: Chemical Looping, DFT, Mechanisms of CO/CO2 Conversion @article{Wang2021, title = {Theoretical DFT Study on the Mechanisms of CO/CO2 Conversion in Chemical Looping Catalyzed by Calcium Ferrite}, author = {Jing Wang, Jiande Gu, Asif Rony, Maohong Fan, and Jerzy Leszczynski}, doi = {https://doi.org/10.1021/acs.jpca.1c04431}, year = {2021}, date = {2021-09-10}, journal = {J. Phys. Chem. A }, volume = {125}, number = {37}, pages = {8159\textendash8167}, abstract = {The CO/CO2 conversion mechanism on the calcium ferrite (CFO) surface in chemical looping was explored by a computational study using the density functional theory approach. The CFO catalytic reaction pathway of 2CO + O2 → 2CO2 conversion has been elucidated. Our results show that the Fe center in CFO plays the key role as a catalyst in the CO/CO2 conversion. Two energetically stable spin states of CFO, quintet and septet, serve as the effective catalysts. The presence of the triplet O2 molecule caused the conversion of these two spin-state structures into each other along the catalytic reaction pathway. A double release of CO2 was predicted following this reaction mechanism. The rate-determining step is the formation of the 2CO2\textendashCFO complex (P4) in the quintet state (19.0 kcal/mol). The predicted energy barriers for all the steps suggest that the proposed pathway is plausible.}, keywords = {Chemical Looping, DFT, Mechanisms of CO/CO2 Conversion}, pubstate = {published}, tppubtype = {article} } The CO/CO2 conversion mechanism on the calcium ferrite (CFO) surface in chemical looping was explored by a computational study using the density functional theory approach. The CFO catalytic reaction pathway of 2CO + O2 → 2CO2 conversion has been elucidated. Our results show that the Fe center in CFO plays the key role as a catalyst in the CO/CO2 conversion. Two energetically stable spin states of CFO, quintet and septet, serve as the effective catalysts. The presence of the triplet O2 molecule caused the conversion of these two spin-state structures into each other along the catalytic reaction pathway. A double release of CO2 was predicted following this reaction mechanism. The rate-determining step is the formation of the 2CO2–CFO complex (P4) in the quintet state (19.0 kcal/mol). The predicted energy barriers for all the steps suggest that the proposed pathway is plausible. | |
Anirudh Reddy Cingireddy, Robin Ghosh, Supratik Kar, Venkata Melapu, Sravanthi Joginipeli, Jerzy Leszczynski Preliminary Screening of COVID-19 Infection Employing Machine Learning Techniques From Simple Blood Profile Journal Article Int J Quantum Struc Prop Rel , 6 (3), pp. 35-47, 2021. Abstract | Links | BibTeX | Tags: Blood Test, COVID-19, Decision Tree, KNN, Logistic Regression, Machine learning, Molecular Tests, Multilayer Perceptron, Naive Bayes, Random Forest, Support Vector Machine, XGBooting @article{Cingireddy2021, title = {Preliminary Screening of COVID-19 Infection Employing Machine Learning Techniques From Simple Blood Profile}, author = {Anirudh Reddy Cingireddy, Robin Ghosh, Supratik Kar, Venkata Melapu, Sravanthi Joginipeli, Jerzy Leszczynski}, doi = {http://doi.org/10.4018/IJQSPR.2021070103}, year = {2021}, date = {2021-09-01}, journal = {Int J Quantum Struc Prop Rel }, volume = {6}, number = {3}, pages = {35-47}, abstract = {Frequent testing of the entire population would help to identify individuals with active COVID-19 and allow us to identify concealed carriers. Molecular tests, antigen tests, and antibody tests are being widely used to confirm COVID-19 in the population. Molecular tests such as the real-time reverse transcription-polymerase chain reaction (rRT-PCR) test will take a minimum of 3 hours to a maximum of 4 days for the results. The authors suggest using machine learning and data mining tools to filter large populations at a preliminary level to overcome this issue. The ML tools could reduce the testing population size by 20 to 30%. In this study, they have used a subset of features from full blood profile which are drawn from patients at Israelita Albert Einstein hospital located in Brazil. They used classification models, namely KNN, logistic regression, XGBooting, naive Bayes, decision tree, random forest, support vector machine, and multilayer perceptron with k-fold cross-validation, to validate the models. Na\"{i}ve bayes, KNN, and random forest stand out as the most predictive ones with 88% accuracy each.}, keywords = {Blood Test, COVID-19, Decision Tree, KNN, Logistic Regression, Machine learning, Molecular Tests, Multilayer Perceptron, Naive Bayes, Random Forest, Support Vector Machine, XGBooting}, pubstate = {published}, tppubtype = {article} } Frequent testing of the entire population would help to identify individuals with active COVID-19 and allow us to identify concealed carriers. Molecular tests, antigen tests, and antibody tests are being widely used to confirm COVID-19 in the population. Molecular tests such as the real-time reverse transcription-polymerase chain reaction (rRT-PCR) test will take a minimum of 3 hours to a maximum of 4 days for the results. The authors suggest using machine learning and data mining tools to filter large populations at a preliminary level to overcome this issue. The ML tools could reduce the testing population size by 20 to 30%. In this study, they have used a subset of features from full blood profile which are drawn from patients at Israelita Albert Einstein hospital located in Brazil. They used classification models, namely KNN, logistic regression, XGBooting, naive Bayes, decision tree, random forest, support vector machine, and multilayer perceptron with k-fold cross-validation, to validate the models. Naïve bayes, KNN, and random forest stand out as the most predictive ones with 88% accuracy each. | |
Supratik Kar; Jerzy Leszczynski Chapter: QSAR and machine learning modeling of toxicity of nanomaterials: a risk assessment approach Book Chapter J. Njuguna K. Pielichowski, Zhu H (Ed.): Chapter 16, pp. 417-441, WOODHEAD PUBLISHING, ELSEVIER, 2nd, 2021. Abstract | Links | BibTeX | Tags: Machine learning, Metal oxide, Nanomaterial, QSAR, Toxicity @inbook{Kar2021, title = {Chapter: QSAR and machine learning modeling of toxicity of nanomaterials: a risk assessment approach}, author = {Supratik Kar and Jerzy Leszczynski}, editor = {J. Njuguna, K. Pielichowski, H. Zhu}, doi = {https://doi.org/10.1016/B978-0-12-820505-1.00016-X}, year = {2021}, date = {2021-07-30}, pages = {417-441}, publisher = {WOODHEAD PUBLISHING, ELSEVIER}, edition = {2nd}, chapter = {16}, series = {Health and Environmental Safety of Nanomaterials: Polymer Nanocomposites and Other Materials Containing Nanoparticles}, abstract = {The advancement of nanoscience and enormous use of nanomaterials (NMs) in the form of coated nanoparticles, engineered metal oxide nanomaterials (MONMs), single- and multiwalled carbon nanotubes, fullerenes (C60/C70), and silica NMs open up multifaceted possibilities of inflicting toxicity on the environment. With the implications of NMs in medicine, cars, batteries, solar panels, textiles, toys, electronics, etc., one can’t control the safe release of NMs into the ecosystem, which directly affects the organisms present in the aquatic and terrestrial environment and indirectly affects humans' lives. The testing of each individual form of NMs on diverse species as well as different response/endpoints by traditional experimental assays is an impossible task. Thus in most cases, environment regulatory bodies, industries, and environmental scientists largely depend on in silico methods like quantitative structure\textendashactivity relationships (QSARs) and machine learning approaches. In a relatively short time, these models can be prepared with the expertise of cheminformaticians and can be employed for the prediction of future NMs as well as the already existing ones in the ecosystem. Since the beginning of 2010, a huge number of in silico models have been developed in combination with in vivo and in vitro analysis, and the present number of such models exceeds 150. The successful models cover eukaryotic to prokaryotic organisms and cell lines including cytotoxicity, genotoxicity, enzymatic inhibition, egg hatching, cellular viability, and cellular uptake. Most of the models can identify the mechanistic interpretation behind the toxicity of NMs toward specific organisms/cell lines. This is helpful for the safe design of NMs for the future along with risk assessment.}, keywords = {Machine learning, Metal oxide, Nanomaterial, QSAR, Toxicity}, pubstate = {published}, tppubtype = {inbook} } The advancement of nanoscience and enormous use of nanomaterials (NMs) in the form of coated nanoparticles, engineered metal oxide nanomaterials (MONMs), single- and multiwalled carbon nanotubes, fullerenes (C60/C70), and silica NMs open up multifaceted possibilities of inflicting toxicity on the environment. With the implications of NMs in medicine, cars, batteries, solar panels, textiles, toys, electronics, etc., one can’t control the safe release of NMs into the ecosystem, which directly affects the organisms present in the aquatic and terrestrial environment and indirectly affects humans' lives. The testing of each individual form of NMs on diverse species as well as different response/endpoints by traditional experimental assays is an impossible task. Thus in most cases, environment regulatory bodies, industries, and environmental scientists largely depend on in silico methods like quantitative structure–activity relationships (QSARs) and machine learning approaches. In a relatively short time, these models can be prepared with the expertise of cheminformaticians and can be employed for the prediction of future NMs as well as the already existing ones in the ecosystem. Since the beginning of 2010, a huge number of in silico models have been developed in combination with in vivo and in vitro analysis, and the present number of such models exceeds 150. The successful models cover eukaryotic to prokaryotic organisms and cell lines including cytotoxicity, genotoxicity, enzymatic inhibition, egg hatching, cellular viability, and cellular uptake. Most of the models can identify the mechanistic interpretation behind the toxicity of NMs toward specific organisms/cell lines. This is helpful for the safe design of NMs for the future along with risk assessment. | |
P. Samanta, J. Leszczynski J. Roy S. Kar, Leszczynski (eds.) J (Ed.): 32 , pp. 99-126, Springer, Cham, 2021, ISBN: 978-3-030-69445-6. Abstract | Links | BibTeX | Tags: BSE, DSSC, Exciton, GW @inbook{Samanta2021, title = {Chapter: Delving charge-transfer excitations in hybrid organic-inorganic hetero junction of dye-sensitized solar cell: Assessment of excitonic optical properties using the GW and Bethe-Salpeter Green’s function formalisms}, author = {P. Samanta, J. Leszczynski}, editor = { J. Roy, S. Kar, J. Leszczynski (eds.)}, doi = {https://doi.org/10.1007/978-3-030-69445-6_5}, isbn = {978-3-030-69445-6}, year = {2021}, date = {2021-05-13}, volume = {32}, pages = {99-126}, publisher = {Springer, Cham}, series = {Development of Solar Cells. Challenges and Advances in Computational Chemistry and Physics}, abstract = {First-principles modeling of charge-neutral excitations with the recognition of charge-transfer and Rydberg states and probing the mechanism of charge-carrier generation from the photoexcited electron\textendashhole pair for the hybrid organic\textendashinorganic photovoltaic materials remain as a cornerstone problem within the framework of time-dependent density functional theory (TDDFT) . The many-body Green’s function Bethe\textendashSalpeter formalism based on a Dyson-like equation for the two-particle correlation function, which accounts for the exchange and attractive screened Coulomb interactions between photoexcited electrons and holes, has emerged as a decent approach to study the photoemission properties including the Frenkel and charge-transfer excitations in an assortment of finite and extended systems of optoelectronic materials. The key ideas of practical implementation of Bethe\textendashSalpeter equation (BSE) involving the computations of single-particle states, quasi-particle energy levels, and the screened Coulomb interaction with the aid of Gaussian atomic basis sets and resolution-of-identity techniques are discussed. The work revisits the computational aspects for the evaluation of electronic, spectroscopic, and photochromic properties of the dye-sensitized solar cell (DSSC) constituents by considering the excitonic effects that renormalize the energy levels and coalesce the single-particle transitions. The most recent advancements in theoretical methods that employ the maximally localized Wannier’s function (MLWF) and curtail the overall scaling of BSE calculations are also addressed, and the viable applications are subsequently illustrated with selected examples. Finally, the review reveals some computational challenges that need to be resolved to expand the applicability of BSE in designing solar cell materials, and to unravel the intricate mechanism of ultrafast excited-state processes.}, keywords = {BSE, DSSC, Exciton, GW}, pubstate = {published}, tppubtype = {inbook} } First-principles modeling of charge-neutral excitations with the recognition of charge-transfer and Rydberg states and probing the mechanism of charge-carrier generation from the photoexcited electron–hole pair for the hybrid organic–inorganic photovoltaic materials remain as a cornerstone problem within the framework of time-dependent density functional theory (TDDFT) . The many-body Green’s function Bethe–Salpeter formalism based on a Dyson-like equation for the two-particle correlation function, which accounts for the exchange and attractive screened Coulomb interactions between photoexcited electrons and holes, has emerged as a decent approach to study the photoemission properties including the Frenkel and charge-transfer excitations in an assortment of finite and extended systems of optoelectronic materials. The key ideas of practical implementation of Bethe–Salpeter equation (BSE) involving the computations of single-particle states, quasi-particle energy levels, and the screened Coulomb interaction with the aid of Gaussian atomic basis sets and resolution-of-identity techniques are discussed. The work revisits the computational aspects for the evaluation of electronic, spectroscopic, and photochromic properties of the dye-sensitized solar cell (DSSC) constituents by considering the excitonic effects that renormalize the energy levels and coalesce the single-particle transitions. The most recent advancements in theoretical methods that employ the maximally localized Wannier’s function (MLWF) and curtail the overall scaling of BSE calculations are also addressed, and the viable applications are subsequently illustrated with selected examples. Finally, the review reveals some computational challenges that need to be resolved to expand the applicability of BSE in designing solar cell materials, and to unravel the intricate mechanism of ultrafast excited-state processes. | |
J. Roy, S. Kar, J. Leszczynski Chapter: Computational screening of organic dye-sensitizers for dye-sensitized solar cells: DFT/TDDFT approach Book Chapter J. Roy S. Kar, Leszczynski (eds.) J (Ed.): 32 , pp. 187-206, Springer, Cham, 2021, ISBN: 978-3-030-69445-6. Abstract | Links | BibTeX | Tags: DFT, DSSC, Organic Sensitizer, TD-DFT @inbook{Roy2021c, title = {Chapter: Computational screening of organic dye-sensitizers for dye-sensitized solar cells: DFT/TDDFT approach}, author = {J. Roy, S. Kar, J. Leszczynski}, editor = {J. Roy, S. Kar, J. Leszczynski (eds.)}, doi = {https://doi.org/10.1007/978-3-030-69445-6_8}, isbn = {978-3-030-69445-6}, year = {2021}, date = {2021-05-13}, volume = {32}, pages = {187-206}, publisher = {Springer, Cham}, series = {Development of Solar Cells. Challenges and Advances in Computational Chemistry and Physics}, abstract = {Dye-sensitized solar cells (DSSCs) represent a promising third-generation photovoltaic technology due to their ease in fabrication, low cost, ability to operate in diffused light, flexibility, and being lightweight. Organic dye-sensitizers are vital components of the DSSCs. Comprehensive theoretical study of the dye’s spectroscopic properties, including excitation energies ground- and excited-state oxidation potential, allows to design and screen organic dye-sensitizers for an efficient DSSC. Density functional theory (DFT) and time-dependent DFT (TDDFT) approaches have been efficiently used to estimate different optoelectronic properties of sensitizers. This chapter outlined the use of the DFT and TDDFT framework to design organic dye-sensitizers for DSSCs to predict different photophysical properties. Prediction of essential factors such as short-circuit current density (JSC), open-circuit voltage (VOC), along with charge transfer phenomena, will help experimental groups to fabricate DSSCs with higher photoconversion efficiency (PCE). Besides, this chapter includes a basic understanding of the mechanism of DSSCs, based on the energetics of the various constituents of the heterogeneous device.}, keywords = {DFT, DSSC, Organic Sensitizer, TD-DFT}, pubstate = {published}, tppubtype = {inbook} } Dye-sensitized solar cells (DSSCs) represent a promising third-generation photovoltaic technology due to their ease in fabrication, low cost, ability to operate in diffused light, flexibility, and being lightweight. Organic dye-sensitizers are vital components of the DSSCs. Comprehensive theoretical study of the dye’s spectroscopic properties, including excitation energies ground- and excited-state oxidation potential, allows to design and screen organic dye-sensitizers for an efficient DSSC. Density functional theory (DFT) and time-dependent DFT (TDDFT) approaches have been efficiently used to estimate different optoelectronic properties of sensitizers. This chapter outlined the use of the DFT and TDDFT framework to design organic dye-sensitizers for DSSCs to predict different photophysical properties. Prediction of essential factors such as short-circuit current density (JSC), open-circuit voltage (VOC), along with charge transfer phenomena, will help experimental groups to fabricate DSSCs with higher photoconversion efficiency (PCE). Besides, this chapter includes a basic understanding of the mechanism of DSSCs, based on the energetics of the various constituents of the heterogeneous device. | |
Devashis Majumdar, Pabitra Narayan Samanta, Szczepan Roszak,; Jerzy Leszczynski Chapter: Slater-Type Orbitals Book Chapter Perlt, Eva (Ed.): 107 , Chapter 2, pp. 17-40, Springer, Cham., 2021, ISSN: 2192-6603. Abstract | Links | BibTeX | Tags: Benchmark studies, Excitation energy calculations, Resonance Raman spectrum analysis, Slater-type orbitals @inbook{Majumdar2021, title = {Chapter: Slater-Type Orbitals}, author = {Devashis Majumdar, Pabitra Narayan Samanta, Szczepan Roszak, and Jerzy Leszczynski}, editor = {Eva Perlt}, doi = {https://doi.org/10.1007/978-3-030-67262-1_2}, issn = {2192-6603}, year = {2021}, date = {2021-05-07}, volume = {107}, pages = {17-40}, publisher = {Springer, Cham.}, chapter = {2}, series = {Lecture Notes in Chemistry}, abstract = {The key concept of Slater-type orbitals (STOs) underpinning quantum chemical calculations of polyatomic systems has been elucidated via a discourse on mathematical challenges of solving immanent multicenter integrals in density functional theory (DFT). Two types of orbitals viz. Gaussian-type orbitals (GTOs) and STOs are being discussed about their importance in atomic orbital-based calculations and compared their advantages and disadvantages in solving chemistry-related problems of molecules. The third type of orbitals obtained through plane-wave basis sets are excluded in this discussion, as they are mostly used to solve condensed-phase problems. The rudiments of STOs have been discussed without radical analysis of programmatic implementations of mathematical algorithms. The discussions are mainly focused on the DFT calculations, and the concepts of various Slater atomic basis sets are being introduced. In the final part of the article, a few specific examples are considered related to the application of DFT-STOs to different chemical problems. We place emphasis on benchmark studies of simple molecular structures, excitation energy calculations, excitation energy spectrum of UO22+ as well as resonance Raman spectrum analysis.}, type = {Book: Basis Sets in Computational Chemistry}, keywords = {Benchmark studies, Excitation energy calculations, Resonance Raman spectrum analysis, Slater-type orbitals}, pubstate = {published}, tppubtype = {inbook} } The key concept of Slater-type orbitals (STOs) underpinning quantum chemical calculations of polyatomic systems has been elucidated via a discourse on mathematical challenges of solving immanent multicenter integrals in density functional theory (DFT). Two types of orbitals viz. Gaussian-type orbitals (GTOs) and STOs are being discussed about their importance in atomic orbital-based calculations and compared their advantages and disadvantages in solving chemistry-related problems of molecules. The third type of orbitals obtained through plane-wave basis sets are excluded in this discussion, as they are mostly used to solve condensed-phase problems. The rudiments of STOs have been discussed without radical analysis of programmatic implementations of mathematical algorithms. The discussions are mainly focused on the DFT calculations, and the concepts of various Slater atomic basis sets are being introduced. In the final part of the article, a few specific examples are considered related to the application of DFT-STOs to different chemical problems. We place emphasis on benchmark studies of simple molecular structures, excitation energy calculations, excitation energy spectrum of UO22+ as well as resonance Raman spectrum analysis. | |
Alexander B. Rozhenko, Andrey A. Kyrylchuk, Yuliia O. Lapinska, Yuliya V. Rassukana, Vladimir V. Trachevsky, Volodymyr V. Pirozhenko, Jerzy Leszczynski; Petro P. Onysko Z,E-Isomerism in a Series of Substituted Iminophosphonates: Quantum Chemical Research Journal Article Organics, 2 (2), pp. 84-97, 2021. Abstract | Links | BibTeX | Tags: DFT calculations; SCS-MP2 calculations; Z, E-isomerism; Iminophosphonates; Thermodynamic stability @article{Rozhenko2021, title = {Z,E-Isomerism in a Series of Substituted Iminophosphonates: Quantum Chemical Research}, author = {Alexander B. Rozhenko, Andrey A. Kyrylchuk, Yuliia O. Lapinska, Yuliya V. Rassukana, Vladimir V. Trachevsky, Volodymyr V. Pirozhenko, Jerzy Leszczynski and Petro P. Onysko}, doi = {https://doi.org/10.3390/org2020008}, year = {2021}, date = {2021-04-23}, journal = {Organics}, volume = {2}, number = {2}, pages = {84-97}, abstract = {Esters of iminophosphonic acids (iminophosphonates, or IPs), including a fragment, >P(=O)-C=N, can be easily functionalized, for instance to aminophosphonic acids with a wide range of biological activity. Depending on the character of the substitution, the Z- or E-configuration is favorable for IPs, which in turn can influence the stereochemistry of the products of chemical transformations of IPs. While the Z,E-isomerism in IPs has been thoroughly studied by NMR spectroscopy, the factors stabilizing a definite isomer are still not clear. In the current work, density functional theory (DFT, using M06-2X functional) and ab initio spin-component\textendashscaled second-order M\oller\textendashPlesset perturbation theory (SCS-MP2) calculations were carried out for a broad series of IPs. The calculations reproduce well a subtle balance between the preferred Z-configuration inherent for C-trifluoromethyl substituted IPs and the E-form, which is more stable for C-alkyl- or aryl-substituted IPs. The predicted trend of changing activation energy values agrees well with the recently determined experimental ΔG≠298 magnitudes. Depending on the substitution in the aromatic moiety, the Z/E-isomerization of N-aryl-substituted IPs proceeds via two types of close-in energy transition states. Not a single main factor but a combination of various contributions should be considered in order to explain the Z/E-isomerization equilibrium for different IPs.}, keywords = {DFT calculations; SCS-MP2 calculations; Z, E-isomerism; Iminophosphonates; Thermodynamic stability}, pubstate = {published}, tppubtype = {article} } Esters of iminophosphonic acids (iminophosphonates, or IPs), including a fragment, >P(=O)-C=N, can be easily functionalized, for instance to aminophosphonic acids with a wide range of biological activity. Depending on the character of the substitution, the Z- or E-configuration is favorable for IPs, which in turn can influence the stereochemistry of the products of chemical transformations of IPs. While the Z,E-isomerism in IPs has been thoroughly studied by NMR spectroscopy, the factors stabilizing a definite isomer are still not clear. In the current work, density functional theory (DFT, using M06-2X functional) and ab initio spin-component–scaled second-order Møller–Plesset perturbation theory (SCS-MP2) calculations were carried out for a broad series of IPs. The calculations reproduce well a subtle balance between the preferred Z-configuration inherent for C-trifluoromethyl substituted IPs and the E-form, which is more stable for C-alkyl- or aryl-substituted IPs. The predicted trend of changing activation energy values agrees well with the recently determined experimental ΔG≠298 magnitudes. Depending on the substitution in the aromatic moiety, the Z/E-isomerization of N-aryl-substituted IPs proceeds via two types of close-in energy transition states. Not a single main factor but a combination of various contributions should be considered in order to explain the Z/E-isomerization equilibrium for different IPs. | |
Alla P. Toropova, Andrey A. Toropov, Jerzy Leszczynski, Natalia Sizochenko Using quasi-SMILES for the predictive modeling of the safety of 574 metal oxide nanoparticles measured in different experimental conditions Journal Article ENVIRON TOXICOL PHAR, 86 , pp. 103665, 2021. Abstract | Links | BibTeX | Tags: CORAL software, Metal oxide nanoparticles, Molecular representation, Monte carlo optimization, Nano-QSAR, Nano-SAR, Quasi-SMILES @article{Toropova2021, title = {Using quasi-SMILES for the predictive modeling of the safety of 574 metal oxide nanoparticles measured in different experimental conditions}, author = {Alla P. Toropova, Andrey A. Toropov, Jerzy Leszczynski, Natalia Sizochenko}, doi = {https://doi.org/10.1016/j.etap.2021.103665}, year = {2021}, date = {2021-04-22}, journal = {ENVIRON TOXICOL PHAR}, volume = {86}, pages = {103665}, abstract = {The production of nanomaterials continues its rapid growth; however, newly manufactured nanomaterials' environmental and health safety are among the most significant concerns. A safety assessment is usually a lengthy and costly process, so computational studies are often used to complement experimental testing. One of the most time-efficient techniques is structure-activity relationships (SAR) modeling. In this project, we analyzed the Sustainable Nanotechnology (S2NANO) dataset that contains 574 experimental cell viability and toxicity datapoints for Al2O3, CuO, Fe2O3, Fe3O4, SiO2, TiO2, and ZnO measured in different conditions. We aimed to develop classification- and regression-based structure-activity relationship models using quasi-SMILES molecular representation. Introduced quasi-SMILES took into consideration all available information, including structural features of nanoparticles (molecular structure, core size, etc.) and related experimental parameters (cell line, dose, exposure time, assay, hydrodynamic size, surface charge, etc.). Resultant regression models demonstrated sufficient predictive power, while classification models demonstrated higher accuracy.}, keywords = {CORAL software, Metal oxide nanoparticles, Molecular representation, Monte carlo optimization, Nano-QSAR, Nano-SAR, Quasi-SMILES}, pubstate = {published}, tppubtype = {article} } The production of nanomaterials continues its rapid growth; however, newly manufactured nanomaterials' environmental and health safety are among the most significant concerns. A safety assessment is usually a lengthy and costly process, so computational studies are often used to complement experimental testing. One of the most time-efficient techniques is structure-activity relationships (SAR) modeling. In this project, we analyzed the Sustainable Nanotechnology (S2NANO) dataset that contains 574 experimental cell viability and toxicity datapoints for Al2O3, CuO, Fe2O3, Fe3O4, SiO2, TiO2, and ZnO measured in different conditions. We aimed to develop classification- and regression-based structure-activity relationship models using quasi-SMILES molecular representation. Introduced quasi-SMILES took into consideration all available information, including structural features of nanoparticles (molecular structure, core size, etc.) and related experimental parameters (cell line, dose, exposure time, assay, hydrodynamic size, surface charge, etc.). Resultant regression models demonstrated sufficient predictive power, while classification models demonstrated higher accuracy. | |
Natalia Sizochenko, Alicja Mikolajczyk, Michael Syzochenko, Tomasz Puzyn, Jerzy Leszczynski Zeta potentials (ζ) of metal oxide nanoparticles: A meta-analysis of experimental data and a predictive neural networks modeling Journal Article Nanoimpact, 22 , pp. 100317, 2021. Abstract | Links | BibTeX | Tags: Computational modeling, Metal oxide nanoparticles, Toxicological profile, Weight of evidence, Zeta potential @article{Sizochenko2021, title = {Zeta potentials (ζ) of metal oxide nanoparticles: A meta-analysis of experimental data and a predictive neural networks modeling}, author = {Natalia Sizochenko, Alicja Mikolajczyk, Michael Syzochenko, Tomasz Puzyn, Jerzy Leszczynski}, doi = {https://doi.org/10.1016/j.impact.2021.100317}, year = {2021}, date = {2021-04-16}, journal = {Nanoimpact}, volume = {22}, pages = {100317}, abstract = {Zeta potential is usually measured to estimate the surface charge and the stability of nanomaterials, as changes in these characteristics directly influence the biological activity of a given nanoparticle. Nowadays, theoretical methods are commonly used for a pre-screening safety assessments of nanomaterials. At the same time, the consistency of data on zeta potential measurements in the context of environmental impact is an important challenge. The inconsistency of data measurements leads to inaccuracies in predictive modeling. In this article, we report a new curated dataset of zeta potentials measured for 208 silica- and metal oxide nanoparticles in different media. We discuss the data curation framework for zeta potentials designed to assess the quality and usefulness of the literature data for further computational modeling. We also provide an analysis of specific trends for the datapoints harvested from different literature sources. In addition to that, we present for the first time a structure-property relationship model for nanoparticles (nano-SPR) that predicts values of zeta potential values measured in different environmental conditions (i.e., biological media and pH).}, keywords = {Computational modeling, Metal oxide nanoparticles, Toxicological profile, Weight of evidence, Zeta potential}, pubstate = {published}, tppubtype = {article} } Zeta potential is usually measured to estimate the surface charge and the stability of nanomaterials, as changes in these characteristics directly influence the biological activity of a given nanoparticle. Nowadays, theoretical methods are commonly used for a pre-screening safety assessments of nanomaterials. At the same time, the consistency of data on zeta potential measurements in the context of environmental impact is an important challenge. The inconsistency of data measurements leads to inaccuracies in predictive modeling. In this article, we report a new curated dataset of zeta potentials measured for 208 silica- and metal oxide nanoparticles in different media. We discuss the data curation framework for zeta potentials designed to assess the quality and usefulness of the literature data for further computational modeling. We also provide an analysis of specific trends for the datapoints harvested from different literature sources. In addition to that, we present for the first time a structure-property relationship model for nanoparticles (nano-SPR) that predicts values of zeta potential values measured in different environmental conditions (i.e., biological media and pH). | |
Books & Articles
2022 |
|
In vitro and in silico study of mixtures cytotoxicity of metal oxide nanoparticles to Escherichia coli: a mechanistic approach Journal Article Nanotoxicology, 2022, (NSF/CREST HRD-1547754). | |
NTO Degradation by Nitroreductase: A DFT Study Journal Article J. Phys. Chem. B, 126 (32), pp. 5991–6006, 2022, (ARO W911NF-20-1-0116; XSEDE DMR110088 ). | |
Struct. Chem. , 33 , pp. 1741–1753, 2022, (NSF/CREST HRD-1547754). | |
Environ. Sci. Pollut. Res., 29 , pp. 68522–68531, 2022, (ARO grant W911NF-20-1-0116 ). | |
Identification of potential antivirals against 3CLpro enzyme for the treatment of SARS-CoV-2: A multi-step virtual screening study Journal Article SAR QSAR Environ. Res., 33 (5), pp. 357-386, 2022, (NSF/CREST HRD-1547754). | |
Molecular Physics, 2022, (ERDC W912HZ-20-20069 NSF-PREM grant no. DMR-1826886 ). | |
2021 |
|
Theoretical DFT Study on the Mechanisms of CO/CO2 Conversion in Chemical Looping Catalyzed by Calcium Ferrite Journal Article J. Phys. Chem. A , 125 (37), pp. 8159–8167, 2021. | |
Preliminary Screening of COVID-19 Infection Employing Machine Learning Techniques From Simple Blood Profile Journal Article Int J Quantum Struc Prop Rel , 6 (3), pp. 35-47, 2021. | |
Chapter: QSAR and machine learning modeling of toxicity of nanomaterials: a risk assessment approach Book Chapter J. Njuguna K. Pielichowski, Zhu H (Ed.): Chapter 16, pp. 417-441, WOODHEAD PUBLISHING, ELSEVIER, 2nd, 2021. | |
J. Roy S. Kar, Leszczynski (eds.) J (Ed.): 32 , pp. 99-126, Springer, Cham, 2021, ISBN: 978-3-030-69445-6. | |
Chapter: Computational screening of organic dye-sensitizers for dye-sensitized solar cells: DFT/TDDFT approach Book Chapter J. Roy S. Kar, Leszczynski (eds.) J (Ed.): 32 , pp. 187-206, Springer, Cham, 2021, ISBN: 978-3-030-69445-6. | |
Chapter: Slater-Type Orbitals Book Chapter Perlt, Eva (Ed.): 107 , Chapter 2, pp. 17-40, Springer, Cham., 2021, ISSN: 2192-6603. | |
Z,E-Isomerism in a Series of Substituted Iminophosphonates: Quantum Chemical Research Journal Article Organics, 2 (2), pp. 84-97, 2021. | |
Using quasi-SMILES for the predictive modeling of the safety of 574 metal oxide nanoparticles measured in different experimental conditions Journal Article ENVIRON TOXICOL PHAR, 86 , pp. 103665, 2021. | |
Zeta potentials (ζ) of metal oxide nanoparticles: A meta-analysis of experimental data and a predictive neural networks modeling Journal Article Nanoimpact, 22 , pp. 100317, 2021. | |