Books & Articles

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@article{Ivashchenko2021,
title = {Phase diagram, electronic, mechanical and thermodynamic properties of TiB2\textendashZrB2 solid solutions: A first-principles study},
author = {V.I. Ivashchenko, P.E.A. Turchi, V.I. Shevchenko, N.R. Mediukh, Leonid Gorb, Jerzy Leszczynski},
doi = {https://doi.org/10.1016/j.matchemphys.2021.124340},
year = {2021},
date = {2021-04-15},
journal = {Mater. Chem. Phys.},
volume = {263},
pages = {124340},
abstract = {The stability, electronic and phonon structures, mechanical and thermodynamic properties as well optical spectra of the Ti1−xZrxB2 solid solutions were investigated in the framework of a first-principles approach. The miscibility gap was predicted with the consolute temperature TC = 1973 K and composition xC = 0.4. The negative deviation of the calculated bulk, shear, Young moduli, Debye temperature, Vickers hardness and fracture toughness from the mixing rule is observed. The ideal shear stress of 41.1 GPa for Ti0.5Zr0.5B2 was found to be lower compared to that for TiB2 (43.5 GPa) and ZrB2 (43.1 GPa). The calculated elastic moduli and Poisson ratio exhibit the spatial anisotropy inherent to hexagonal structures. It was shown that the thermodynamic characteristics of the Zr-rich solid solutions could be well reproduced in a temperature range up to 1000\textendash1400 K using the harmonic approximation. The calculated dielectric constants and , and optical reflectivity spectra for Ti1−xZrxB2 were analyzed in comparison with available optical spectra of parent diborides from other authors.},
keywords = {BSE, DSSC, Elastic properties, Electronic properties, Exciton, First-principles, GW, Optical properties, Ti-Zr-B2},
pubstate = {published},
tppubtype = {article}
}

@article{Borysenko2021,
title = {Efficient approach for exploring the multiple-channel bimolecular interactions of conformationally flexible reagents. Epoxide ring opening reaction},
author = {Iryna O. Borysenko, Liudmyla K. Sviatenko, Sergiy I. Okovytyy, Jerzy Leszczynski},
doi = {https://doi.org/10.1007/s11224-020-01663-0},
year = {2021},
date = {2021-04-15},
journal = {Struct. Chem.},
volume = {32},
pages = {581 - 589},
abstract = {Algorithm for generation and assessment of probability of possible reaction pathways for multiple-channel bimolecular interactions is presented. The proposed algorithm comprises a combination of few steps. They include conformational search for reaction intermediate using the molecular mechanics (MMX) approach, based on the obtained conformation construction of structures of transition states and pre-reaction complexes, and calculation activation energies to further determine the probable reaction pathways. The proposed algorithm could be adopted for investigation of chemical and biochemical reactions of different types. Here, we have considered the reaction of bicyclo[2.2.1]hept-5-en-endo-2-ylmethylamine (1) with glycidyl ether (2) in a neutral environment that proceeds through SN2-like mechanism forming bipolar ion (3) which is a good starting point for identification of the reaction channels. Conformational properties of intermediate (3) have been investigated using stochastic conformational search. From the 95 localized conformations within 10 kcal/mol of global minimum that have been obtained, 63 unique transition state conformations were generated and optimized by using the PM7 and M062X/6-31G(d) methods for accurate estimation of overall rate constant of reaction. The most energetically favorable pathways have been investigated at the M062X/6-31G(d) level of theory taking into account the influence of solvent.},
keywords = {DFT, Epoxide ring opening reaction},
pubstate = {published},
tppubtype = {article}
}

@article{Roy2021,
title = {Interaction of epoxy-based hydrogels and water: A molecular dynamics simulation study},
author = {Juganta K. Roy, Henry P. Pinto, Jerzy Leszczynski},
doi = {https://doi.org/10.1016/j.jmgm.2021.107915},
year = {2021},
date = {2021-04-13},
journal = {J MOL GRAPH MODEL},
volume = {106},
pages = {107915},
abstract = {Biomaterials play a crucial role in tissue engineering as a functional replacement, regenerative medicines, supportive scaffold for guided tissue growth, and drug delivery devices. The term biomaterial refers to metals, ceramics, and polymers account for the vast majority. In the case of polymers, hydrogels have emerged as active materials for an immense variety of applications. Epoxy-based hydrogels possess a unique network structure that enables very high levels of hydrophilicity and biocompatibility. Hydrogel such as Medipacs Epoxy Polymers (MEPs) models were constructed to understand water’s behavior at the water/hydrogel interface and hydrogel network. We computed the Gibbs dividing surface (GDS) to define the MEP/water interface, and all the physicochemical properties were computed based on GDS. We calculated the radial distribution function (RDF), the 2D surface roughness of the immersed MEPs. RDF analysis confirmed that the first hydration shell is at a distance of 1.86 r{A}, and most of the water molecules are near the hydroxyl group of the MEPs network. Hydrogen bonds (H-bonds) analysis was performed, and the observation suggested that the disruption of the H-bonds between MEP chains leads to an increase in the polymer matrix’s void spaces. These void spaces are filled with diffused water molecules, leading to swelling of the MEP hydrogel. The swelling parameter was estimated from the fitted curve of the yz-lattice of the simulation cell. The MEP/water interface simulation results provide insightful information regarding the design strategy of epoxy-based hydrogel and other hydrogels vital for biomedical applications.},
keywords = {Atomistic MD simulation, Biomaterials, Epoxy-based hydrogel, Gibbs dividing surface, Surface roughness, Swelling behavior},
pubstate = {published},
tppubtype = {article}
}

@inbook{Kar2021b,
title = {Chapter: Drug Databases for Development of Therapeutics Against Coronaviruses},
author = {S. Kar, J. Leszczynski},
editor = {K. Roy, Ed},
doi = {https://doi.org/10.1007/7653_2020_66},
isbn = {978-1-0716-1366-5},
year = {2021},
date = {2021-04-06},
pages = {761-780},
publisher = {Springer, New York, NY},
series = {In Silico Modeling of Drugs Against Coronaviruses. Methods in Pharmacology and Toxicology. },
abstract = {Within a span of 11 months starting from December 2019, around 47.6 million people have been infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), including the number of deaths touching 1,215,601 on November 3, 2020. The number increases at an alarming rate with a possible second wave of Coronavirus Disease 2019 (COVID-19) throughout the world. A clear threat of another lockdown is looming over the social life and economy. Thus, scientists worldwide are running against the time to find small drug molecules as therapeutics and possible vaccines to relieve the world. Over the past months, computational chemistry and computer-aided drug design (CADD) have shown encouraging promises in generating multiple lead/hit compounds by employing powerful virtual screening techniques (VS) and drug repurposing of various approved and experimental drugs. The present chapter has enlisted and discussed the top 25 small molecule databases, including both synthetic as well as natural compounds. Most of the databases are freely available for research purposes, which can be strategically screened employing multiple computational techniques to discover therapeutics for COVID-19.},
keywords = {Antiviral, COVID-19, Database Repurposing, SARS-CoV-2, Virtual screening},
pubstate = {published},
tppubtype = {inbook}
}

@article{Skretna2021,
title = {The kernel weighted local polynomial regression (KwLPR) approach: an efficient, novel tool for development of QSAR/QSAAR toxicity extrapolation models.},
author = {Agnieszka Gajewicz Skretna, Supratik Kar, Magdalena Piotrowska, Jerzy Leszczynski},
doi = {https://doi.org/10.1186/s13321-021-00484-5},
year = {2021},
date = {2021-02-12},
journal = {J. Cheminform.},
volume = {13},
number = {9},
pages = {1-20},
abstract = {The ability of accurate predictions of biological response (biological activity/property/toxicity) of a given chemical makes the quantitative structure‐activity/property/toxicity relationship (QSAR/QSPR/QSTR) models unique among the in silico tools. In addition, experimental data of selected species can also be used as an independent variable along with other structural as well as physicochemical variables to predict the response for different species formulating quantitative activity\textendashactivity relationship (QAAR)/quantitative structure\textendashactivity\textendashactivity relationship (QSAAR) approach. Irrespective of the models' type, the developed model's quality, and reliability need to be checked through multiple classical stringent validation metrics. Among the validation metrics, error-based metrics are more significant as the basic idea of a good predictive model is to improve the predictions' quality by lowering the predicted residuals for new query compounds. Following the concept, we have checked the predictive quality of the QSAR and QSAAR models employing kernel-weighted local polynomial regression (KwLPR) approach over the traditional linear and non-linear regression-based approaches tools such as multiple linear regression (MLR) and k nearest neighbors (kNN). Five datasets which were previously modeled using linear and non-linear regression method were considered to implement the KwPLR approach, followed by comparison of their validation metrics outcomes. For all five cases, the KwLPR based models reported better results over the traditional approaches. The present study's focus is not to develop a better or improved QSAR/QSAAR model over the previous ones, but to demonstrate the advantage, prediction power, and reliability of the KwLPR algorithm and establishing it as a novel, powerful cheminformatic tool. To facilitate the use of the KwLPR algorithm for QSAR/QSPR/QSTR/QSAAR modeling, the authors provide an in-house developed KwLPR.RMD script under the open-source R programming language.},
keywords = {kernel weighted local polynomial regression, QSAR},
pubstate = {published},
tppubtype = {article}
}

@article{Kar2021c,
title = {Evaluating the cytotoxicity of a large pool of metal oxide nanoparticles to Escherichia coli: Mechanistic understanding through In Vitro and In Silico studies},
author = {Supratik Kar, Kavitha Pathakoti, Paul B. Tchounwou, Danuta Leszczynska, Jerzy Leszczynski},
doi = {https://doi.org/10.1016/j.chemosphere.2020.128428},
year = {2021},
date = {2021-02-11},
journal = {Chemosphere},
volume = {264},
pages = {128428},
abstract = {The toxic effect of eight metal oxide nanoparticles (MONPs) on Escherichia coli was experimentally evaluated following standard bioassay protocols. The obtained cytotoxicity ranking of these studied MONPs is Er2O3, Gd2O3, CeO2, Co2O3, Mn2O3, Co3O4, Fe3O4/WO3 (in descending order). The computed EC50 values from experimental data suggested that Er2O3 and Gd2O3 were the most acutely toxic MONPs to E. coli. To identify the mechanism of toxicity of these 8 MONPs along with 17 other MONPs from our previous study, we employed seven classifications and machine learning (ML) algorithms including linear discriminant analysis (LDA), na\"{i}ve bayes (NB), multinomial logistic regression (MLogitR), sequential minimal optimization (SMO), AdaBoost, J48, and random forest (RF). We also employed 1st and 2nd generation periodic table descriptors developed by us (without any sophisticated computing facilities) along with experimentally analyzed Zeta-potential, to model the cytotoxicity of these MONPs. Based on qualitative validation metrics, the LDA model appeared to be the best among the 7 tested models. The core environment of metal defined by the ratio of the number of core electrons to the number of valence electrons and the electronegativity count of oxygen showed a positive impact on toxicity. The identified properties were important for understanding the mechanisms of nanotoxicity and for predicting the potential environmental risk associated with MONPs exposure. The developed models can be utilized for environmental risk assessment of any untested MONP to E. coli, thereby providing a scientific basis for the design and preparation of safe nanomaterials.},
keywords = {Classification, in silico, In vitro, Machine learning, Metal oxide, Nanoparticles, Toxicity},
pubstate = {published},
tppubtype = {article}
}