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Gupta PO, Sharma SJ, Sekar N. Theoretical investigation of substitution effect on the sixth and seventh positions of coumarin derivatives. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 304:123373. [PMID: 37708758 DOI: 10.1016/j.saa.2023.123373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 08/27/2023] [Accepted: 09/06/2023] [Indexed: 09/16/2023]
Abstract
The linear and non-linear optical properties of 6-donor and 7-donor substituted coumarins were compared using density functional theory (DFT) and time-dependent-DFT (TD-DFT). Charge transfer characteristics were investigated through natural bond order analysis, frontier molecular orbital, and molecular electrostatic potential plots. TD-DFT results suggested that the 6-donor substituted coumarins (PS1, PS3, and PS5) showed red-shifted absorption than the 7-donor substituted coumarins (PS2, PS4, and PS6). The chemical potential (μ) and electrophilicity index (ω) showed direct relation with the band gap and an inverse relation with chemical hardness (η) and hyperhardness (Γ). The global reactivity descriptors μ and ω showed direct and η and Γ showed an inverse correlation with first-order hyperpolarizability (β0) and second-order hyperpolarizability (γ). The β0 and γ for 7-donor substituted coumarin are higher than for 6-donor substituted coumarin.
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Affiliation(s)
- Puja O Gupta
- Department of Dyestuff Technology (Currently named Department of Speciality Chemicals Technology), Institute of Chemical Technology, N. P. Marg, Matunga, Mumbai 400019, Maharashtra, India
| | - Suryapratap J Sharma
- Department of Dyestuff Technology (Currently named Department of Speciality Chemicals Technology), Institute of Chemical Technology, N. P. Marg, Matunga, Mumbai 400019, Maharashtra, India
| | - Nagaiyan Sekar
- Department of Dyestuff Technology (Currently named Department of Speciality Chemicals Technology), Institute of Chemical Technology, N. P. Marg, Matunga, Mumbai 400019, Maharashtra, India.
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2
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Sharma SJ, Sekar N. Charge Transfer as Bridging Correlator for DSSC Efficiency and NLO Property. ChemistrySelect 2022. [DOI: 10.1002/slct.202203262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Affiliation(s)
- Suryapratap J. Sharma
- Dyestuff Technology Department (Currently named Department of Specialty Chemicals Technology) Institute of Chemical Technology Nathalal Parekh Marg, Matunga Mumbai 400019 Maharashtra India
| | - Nagaiyan Sekar
- Dyestuff Technology Department (Currently named Department of Specialty Chemicals Technology) Institute of Chemical Technology Nathalal Parekh Marg, Matunga Mumbai 400019 Maharashtra India
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3
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Kuntz D, Wilson AK. Machine learning, artificial intelligence, and chemistry: how smart algorithms are reshaping simulation and the laboratory. PURE APPL CHEM 2022. [DOI: 10.1515/pac-2022-0202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Machine learning and artificial intelligence are increasingly gaining in prominence through image analysis, language processing, and automation, to name a few applications. Machine learning is also making profound changes in chemistry. From revisiting decades-old analytical techniques for the purpose of creating better calibration curves, to assisting and accelerating traditional in silico simulations, to automating entire scientific workflows, to being used as an approach to deduce underlying physics of unexplained chemical phenomena, machine learning and artificial intelligence are reshaping chemistry, accelerating scientific discovery, and yielding new insights. This review provides an overview of machine learning and artificial intelligence from a chemist’s perspective and focuses on a number of examples of the use of these approaches in computational chemistry and in the laboratory.
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Affiliation(s)
- David Kuntz
- Department of Chemistry , University of North Texas , Denton , TX 76201 , USA
| | - Angela K. Wilson
- Department of Chemistry , Michigan State University , East Lansing , MI 48824 , USA
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4
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Saini V, Sharma A, Nivatia D. A machine learning approach for predicting the nucleophilicity of organic molecules. Phys Chem Chem Phys 2022; 24:1821-1829. [PMID: 34986215 DOI: 10.1039/d1cp05072a] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Nucleophilicity provides important information about the chemical reactivity of organic molecules. Experimental determination of the nucleophilicity parameter is a tedious and resource-intensive approach. Herein, we present a novel machine learning protocol that uses key structural descriptors to predict the nucleophilicities of organic molecules, which agree well with the experimental values. A data driven approach was used where quantum mechanical molecular and thermodynamic descriptors from a wide range of structurally diverse nucleophiles and relevant solvents were extracted and modelled using advanced algorithms against the experimentally available nucleophilicity values. Despite the structural diversity of nucleophiles, we are able to achieve statistically robust models with a high predictive power using tree-based and neural network algorithms trained on an in-house developed unique dataset consisting of 752 nucleophilicity values and 27 molecular descriptors.
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Affiliation(s)
- Vaneet Saini
- Department of Chemistry & Centre for Advanced Studies in Chemistry, Panjab University, Chandigarh 160014, India.
| | - Aditya Sharma
- Department of Chemistry & Centre for Advanced Studies in Chemistry, Panjab University, Chandigarh 160014, India.
| | - Dhruv Nivatia
- IT Department, University Institute of Engineering & Technology, Panjab University, Chandigarh 160014, India
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5
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Chayawan, Selvestrel G, Baderna D, Toma C, Caballero Alfonso AY, Gamba A, Benfenati E. Skin sensitization quantitative QSAR models based on mechanistic structural alerts. Toxicology 2022; 468:153111. [DOI: 10.1016/j.tox.2022.153111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/05/2022] [Accepted: 01/26/2022] [Indexed: 10/19/2022]
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6
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Lata S, Vikas. Concentration-dependent adsorption of organic contaminants by graphene nanosheets: quantum-mechanical models. J Mol Model 2021; 27:48. [PMID: 33496822 DOI: 10.1007/s00894-021-04686-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 01/19/2021] [Indexed: 10/22/2022]
Abstract
Adsorption is the key process in the expression of environmentally relevant physicochemical and toxicological properties of carbon nanomaterials. However, the adsorption of organic contaminants on to nanomaterials is a highly complex phenomenon, owing to the heterogeneity of adsorption sites, for example, on graphene surface as well as due to multiple factors operative during the adsorption, particularly, at the quantum-mechanical level. For predicting the concentration-dependent adsorption coefficients of organic contaminants by carbon nanomaterials, one option has been to rely on the existing linear-solvation energy relationship (LSER) models. The present work on the adsorption of aromatic and aliphatic organic contaminants by graphene nanosheets reveals that the existing LSER models are prone to failure when tested for internal and external validation using an external prediction set of compounds unknown to the model. As an alternative to the LSERs, the present work reports pure quantum-mechanical models developed using computational only quantum-mechanical descriptors. The reliability of the quantum-mechanical models was tested using state-of-the-art validation procedures employing an external prediction set of compounds. The proposed quantum-mechanical models reveal mean polarizability, zero-point vibrational energy, and its electron-correlation contribution to be the key descriptors in the prediction of adsorption coefficients of organic contaminants by graphene nanosheets.
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Affiliation(s)
- Suman Lata
- Quantum Chemistry Group, Department of Chemistry and Centre of Advanced Studies in Chemistry, Panjab University, Chandigarh, 160014, India
| | - Vikas
- Quantum Chemistry Group, Department of Chemistry and Centre of Advanced Studies in Chemistry, Panjab University, Chandigarh, 160014, India.
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7
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Chayawan C, Toma C, Benfenati E, Caballero Alfonso AY. Towards an Understanding of the Mode of Action of Human Aromatase Activity for Azoles through Quantum Chemical Descriptors-Based Regression and Structure Activity Relationship Modeling Analysis. Molecules 2020; 25:molecules25030739. [PMID: 32046297 PMCID: PMC7037385 DOI: 10.3390/molecules25030739] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 02/06/2020] [Accepted: 02/06/2020] [Indexed: 11/16/2022] Open
Abstract
Aromatase is an enzyme member of the cytochrome P450 superfamily coded by the CYP19A1 gene. Its main action is the conversion of androgens into estrogens, transforming androstenedione into estrone and testosterone into estradiol. This enzyme is present in several tissues and it has a key role in the maintenance of the balance of androgens and estrogens, and therefore in the regulation of the endocrine system. With regard to chemical safety and human health, azoles, which are used as agrochemicals and pharmaceuticals, are potential endocrine disruptors due to their agonist or antagonist interactions with the human aromatase enzyme. This theoretical study investigated the active agonist and antagonist properties of “chemical classes of azoles” to determine the relationships of azole interaction with CYP19A1, using stereochemical and electronic properties of the molecules through classification and multilinear regression (MLR) modeling. The antagonist activities for the same substituent on diazoles and triazoles vary with its chemical composition and its position and both heterocyclic systems require aromatic substituents. The triazoles require the spherical shape and diazoles have to be in proper proportion of the branching index and the number of ring systems for the inhibition. Considering the electronic aspects, triazole antagonist activity depends on the electrophilicity index that originates from interelectronic exchange interaction (ωHF) and the LUMO energy (ELUMOPM7), and the diazole antagonist activity originates from the penultimate orbital (EHOMONLPM7) of diazoles. The regression models for agonist activity show that it is opposed by the static charges but favored by the delocalized charges on the diazoles and thiazoles. This study proposes that the electron penetration of azoles toward heme group decides the binding behavior and stereochemistry requirement for antagonist activity against CYP19A1 enzyme.
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Affiliation(s)
- Chayawan Chayawan
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di RicercheFarmacologiche “Mario Negri”—IRCCS, Via Mario Negri, 2, 20156 Milano, Italy; (C.C.); (C.T.)
| | - Cosimo Toma
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di RicercheFarmacologiche “Mario Negri”—IRCCS, Via Mario Negri, 2, 20156 Milano, Italy; (C.C.); (C.T.)
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di RicercheFarmacologiche “Mario Negri”—IRCCS, Via Mario Negri, 2, 20156 Milano, Italy; (C.C.); (C.T.)
- Correspondence: (E.B.); (A.Y.C.A.); Tel.: +39-023-901-4420 (E.B.); +39-388-794-3483 (A.Y.C.A.)
| | - Ana Y. Caballero Alfonso
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di RicercheFarmacologiche “Mario Negri”—IRCCS, Via Mario Negri, 2, 20156 Milano, Italy; (C.C.); (C.T.)
- Jozef Stefan International Postgraduate School, Jamovacesta 39, 1000 Ljubljana, Slovenia
- Correspondence: (E.B.); (A.Y.C.A.); Tel.: +39-023-901-4420 (E.B.); +39-388-794-3483 (A.Y.C.A.)
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8
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Villaverde JJ, Sevilla-Morán B, López-Goti C, Alonso-Prados JL, Sandín-España P. QSAR/QSPR models based on quantum chemistry for risk assessment of pesticides according to current European legislation. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:49-72. [PMID: 31766890 DOI: 10.1080/1062936x.2019.1692368] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 11/10/2019] [Indexed: 06/10/2023]
Abstract
In Europe, agencies and official organizations involved in the pesticide control such as the EFSA, ECHA, JRC and ECETOC or even the OECD are pointing out that the software tools based on quantitative structure relationship models, i.e. QSAR and QSPR, have a huge potential to improve the pesticide risk assessment process. In this sense, these non-animal test methods can promote the competitiveness of agriculture in this region: the consumer safety is increased with them due to the possibility of perform an overall better risk assessment of the degradation products and metabolites from pesticides. However, the use of theses computational-based (in silico) tools must be much more systematised and harmonised, improving their validation and including case studies to test them. To open databases, incorporating critical data in an orderly manner for building the models, becomes also necessary. Moreover, quantum chemistry through the Density Functional Theory should be promoted as tool for calculation of quantum descriptors, especially for the study of similar compounds with the same carbon skeleton but differing substitution patterns, e.g. isomers.
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Affiliation(s)
| | | | - C López-Goti
- Unit of Plant Protection Products, INIA, Madrid, Spain
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9
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Lata S. Externally predictive quantum-mechanical models for the adsorption of aromatic organic compounds by graphene-oxide nanomaterials. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2019; 30:847-863. [PMID: 31577156 DOI: 10.1080/1062936x.2019.1666164] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 08/29/2019] [Indexed: 06/10/2023]
Abstract
Graphene oxide is most often chosen as an alternative to graphene in the applications of carbon-based nanomaterials where adsorption is the primary process. However, its adsorption properties are poorly understood. The existing reports on the adsorption mechanism of graphene oxide rely on the linear free-energy/solvation-energy relationship (LFER/LSER) models. This computational work explores the role of quantum mechanical descriptors in the adsorption of aromatic organic compounds by graphene-oxide. For this, externally predictive quantitative models based on quantum-mechanical descriptors are developed and compared with the existing LSERs for the prediction of adsorption coefficients of organic compounds at three different adsorbate concentrations. The predictivity of the models is assessed using an external prediction set of compounds not used for developing the models. Notably, the mean polarizability, but originating from the quantum mechanical exchange interactions (between electrons of parallel spin), is found to be the most significant factor in driving the adsorption on graphene oxide. The present work also proposes quantum-mechanical-LSER models based on a combination of quantum-mechanical and LSER descriptors, which are in fact found to be equally predictive as the existing LSERs. The quantum-mechanical models proposed in this work are further utilized for the prediction of adsorption coefficients of aliphatic compounds.
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Affiliation(s)
- S Lata
- Quantum Chemistry Group, Department of Chemistry and Centre of Advanced Studies in Chemistry, Panjab University, Chandigarh, India
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10
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Lata S, Vikas. Modeling the Solubility of C
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Fullerenes in Diverse Solvents: Role of Quantum‐mechanical Descriptors. Mol Inform 2019; 38:e1800112. [DOI: 10.1002/minf.201800112] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 02/08/2019] [Indexed: 11/09/2022]
Affiliation(s)
- Suman Lata
- Quantum Chemistry GroupDepartment of Chemistry and Centre of Advanced Studies in ChemistryPanjab University Chandigarh 160014 India
| | - Vikas
- Quantum Chemistry GroupDepartment of Chemistry and Centre of Advanced Studies in ChemistryPanjab University Chandigarh 160014 India
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11
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Lata S. Quantum-mechanical LSERs for the concentration-dependent adsorption of aromatic organic compounds by activated carbon: Applications and comparison with carbon nanotubes. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2019; 30:109-130. [PMID: 30727761 DOI: 10.1080/1062936x.2019.1566173] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Indexed: 06/09/2023]
Abstract
Carbon nanotubes (CNTs) have taken precedence over activated carbon in various applications where adsorption is the primary process. The adsorption of chemical compounds by CNTs and activated carbon is most often predicted through linear free energy/solvation energy relationships (LFERs/LSERs). This work proposes quantum-mechanical LSER models based on a combination of quantum-mechanical descriptors and solvatochromic descriptors of LSERs for predicting the adsorption of aromatic organic compounds by activated carbon at varying adsorbate concentrations. The models are validated using state-of-the-art procedures employing an external prediction set of compounds. This work reveals the hydrogen bond donating and accepting ability of compounds to be the most influencing - but a negative - factor in the adsorption process of activated carbon. The quantum-mechanical LSERs proposed in this work are analysed and found to be equally reliable as the existing LSERs. These were further used to predict the adsorption of nucleobases, steroid hormones, agrochemicals, endocrine disruptors and pharmaceutical drugs. Notably, agrochemicals and endocrine disruptors are predicted to be adsorbed more strongly by activated carbon when compared with their adsorption by CNTs. However, quantum-mechanical LSERs predict the adsorption strength of biomolecules on activated carbon to be similar to that on the CNTs, which can be used to assess the risk associated with using carbon materials.
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Affiliation(s)
- S Lata
- a Quantum Chemistry Group, Department of Chemistry and Centre of Advanced Studies in Chemistry, Panjab University , Chandigarh , India
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12
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Lata S, Vikas. Concentration dependent adsorption of aromatic organic compounds by SWCNTs: Quantum-mechanical descriptors for nano-toxicological studies of biomolecules and agrochemicals. J Mol Graph Model 2018; 85:232-241. [PMID: 30227368 DOI: 10.1016/j.jmgm.2018.08.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2018] [Revised: 08/12/2018] [Accepted: 08/20/2018] [Indexed: 12/30/2022]
Abstract
For evaluating the environmental risk associated using carbon nanotubes (CNTs), a successful prediction is desired for the adsorption of organic compounds (OCs) by CNTs at different adsorbate concentrations. This is most often achieved through poly-parameter linear solvation energy relationships (LSERs) based on solvatochromic descriptors. This study examines the real predictivity of the existing LSERs for predicting the adsorption of OCs by single-walled CNTs (SWCNTs) while comparing it with that of the models developed in the present work using quantum-mechanical descriptors. The real predictivity of the quantum-mechanical models and existing LSERs is compared using state-of-the-art statistical procedures employing an external prediction set of compounds not used in the model development. The quantum-mechanically computed mean polarizability, but originating from the interactions between electrons of parallel spin, is found to play an essential role in the adsorption of OCs by SWCNTs. Besides the solvatochromic descriptors (McGowan volume and molar excess refractivity), the instantaneous inter-electronic interactions, captured through electron-correlation based quantum-mechanical descriptors, are found to significantly affect the adsorption at varying adsorbate concentration. The models developed using a combination of quantum-mechanical and solvatochromic descriptors are found to be quite reliable. The models proposed were further employed to predict the adsorption of agrochemicals such as insecticides, pesticides, herbicides, as well as adsorption of endocrine disruptors and biomolecules such as nucleobases and steroid hormones. These are predicted to be strongly adsorbed by SWCNTs with Progesterone and Guanine exhibiting maximal interaction with the SWCNTs among biomolecules. The quantum-mechanical descriptors proposed in this work can be used for the risk assessment of SWCNTs in systems where adsorption is the primary process.
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Affiliation(s)
- Suman Lata
- Quantum Chemistry Group, Department of Chemistry and Centre of Advanced Studies in Chemistry, Panjab University, Chandigarh, 160014, India
| | - Vikas
- Quantum Chemistry Group, Department of Chemistry and Centre of Advanced Studies in Chemistry, Panjab University, Chandigarh, 160014, India.
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Chayawan, Vikas. Quantum-mechanical parameters for the risk assessment of multi-walled carbon-nanotubes: A study using adsorption of probe compounds and its application to biomolecules. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2016; 218:615-624. [PMID: 27481646 DOI: 10.1016/j.envpol.2016.07.045] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Revised: 06/09/2016] [Accepted: 07/21/2016] [Indexed: 06/06/2023]
Abstract
This work forwards new insights into the risk-assessment of multi-walled carbon-nanotubes (MWCNTs) while analysing the role of quantum-mechanical interactions between the electrons in the adsorption of probe compounds and biomolecules by MWCNTs. For this, the quantitative models are developed using quantum-chemical descriptors and their electron-correlation contribution. The major quantum-chemical factors contributing to the adsorption are found to be mean polarizability, electron-correlation energy, and electron-correlation contribution to the absolute electronegativity and LUMO energy. The proposed models, based on only three quantum-chemical factors, are found to be even more robust and predictive than the previously known five or four factors based linear free-energy and solvation-energy relationships. The proposed models are employed to predict the adsorption of biomolecules including steroid hormones and DNA bases. The steroid hormones are predicted to be strongly adsorbed by the MWCNTs, with the order: hydrocortisone > aldosterone > progesterone > ethinyl-oestradiol > testosterone > oestradiol, whereas the DNA bases are found to be relatively less adsorbed but follow the order as: guanine > adenine > thymine > cytosine > uracil. Besides these, the developed electron-correlation based models predict several insecticides, pesticides, herbicides, fungicides, plasticizers and antimicrobial agents in cosmetics, to be strongly adsorbed by the carbon-nanotubes. The present study proposes that the instantaneous inter-electronic interactions may be quite significant in various physico-chemical processes involving MWCNTs, and can be used as a reliable predictor for their risk assessment.
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Evaluating the role of electron-correlation in the external prediction of the toxicity of nitrobenzenes towards Tetrahymena pyriformis. NEW J CHEM 2016. [DOI: 10.1039/c5nj02552d] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Pang SK. Quantum-chemically-calculated mechanistically interpretable molecular descriptors for drug-action mechanism study – a case study of anthracycline anticancer antibiotics. RSC Adv 2016. [DOI: 10.1039/c6ra14630a] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Identification of drug-action mechanisms and understanding of chemical substituents affecting the anticancer activity of drugs are important for drug development.
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Affiliation(s)
- Siu-Kwong Pang
- Institute of Textiles and Clothing
- Faculty of Applied Science and Textiles
- The Hong Kong Polytechnic University
- Kowloon
- China
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Chayawan, Vikas. Externally predictive single-descriptor based QSPRs for physico-chemical properties of polychlorinated-naphthalenes: Exploring relationships of logS(W), logK(OA), and logK(OW) with electron-correlation. JOURNAL OF HAZARDOUS MATERIALS 2015; 296:68-81. [PMID: 25913673 DOI: 10.1016/j.jhazmat.2015.04.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2014] [Revised: 04/07/2015] [Accepted: 04/10/2015] [Indexed: 06/04/2023]
Abstract
Quantitative structure-property relationships (QSPRs), based only on a single-parameter, are proposed for the prediction of physico-chemical properties, namely, aqueous solubility (logSW), octanol-water partition coefficient (logKOW) and octanol-air partition coefficient (logKOA) of polychloronaphthalenes (PCNs) including all the 75 chloronaphthalene congeners. The QSPR models are developed using molecular descriptors computed through quantum mechanical methods including ab-initio as well as advanced semi-empirical methods. The predictivity of the developed models is tested through state-of-the-art external validation procedures employing an external prediction set of compounds. To analyse the role of instantaneous interactions between electrons (the electron-correlation), the models are also compared with those developed using only the electron-correlation contribution of the quantum chemical descriptor. The electron-correlation contribution towards the chemical hardness and the LUMO energy are observed to be the best predictors for octanol-water partition coefficient, whereas for the octanol-air partition coefficient, the total electronic energy and electron-correlation energy are found to be reliable descriptors, in fact, even better than the polarisability. For aqueous solubility of PCNs, the absolute electronegativity is observed to be the best predictor. This work suggests that the electron-correlation contribution of a quantum-chemical descriptor can be used as a reliable indicator for physico-chemical properties, particularly the partition coefficients.
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Exploring the role of quantum chemical descriptors in modeling acute toxicity of diverse chemicals to Daphnia magna. J Mol Graph Model 2015; 61:89-101. [PMID: 26188798 DOI: 10.1016/j.jmgm.2015.06.009] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Revised: 06/04/2015] [Accepted: 06/20/2015] [Indexed: 11/18/2022]
Abstract
Various quantum-mechanically computed molecular and thermodynamic descriptors along with physico-chemical, electrostatic and topological descriptors are compared while developing quantitative structure-activity relationships (QSARs) for the acute toxicity of 252 diverse organic chemicals towards Daphnia magna. QSAR models based on the quantum-chemical descriptors, computed with routinely employed advanced semi-empirical and ab-initio methods, along with the electron-correlation contribution (CORR) of the descriptors, are analyzed for the external predictivity of the acute toxicity. The models with reliable internal stability and external predictivity are found to be based on the HOMO energy along with the physico-chemical, electrostatic and topological descriptors. Besides this, the total energy and electron-correlation energy are also observed as highly reliable descriptors, suggesting that the intra-molecular interactions between the electrons play an important role in the origin of the acute toxicity, which is in fact an unexplored phenomenon. The models based on quantum-chemical descriptors such as chemical hardness, absolute electronegativity, standard Gibbs free energy and enthalpy are also observed to be reliable. A comparison of the robust models based on the quantum-chemical descriptors computed with various quantum-mechanical methods suggests that the advanced semi-empirical methods such as PM7 can be more reliable than the ab-initio methods which are computationally more expensive.
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18
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Role of exchange and correlation in the real external prediction of mutagenicity: performance of hybrid and meta-hybrid exchange–correlation functionals. RSC Adv 2015. [DOI: 10.1039/c4ra14262d] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
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19
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Vikas, Chayawan. Single-descriptor based quantum-chemical QSPRs for physico-chemical properties of polychlorinated-dibenzo-p-dioxins and -dibenzo-furans (PCDD/Fs): exploring the role of electron-correlation. CHEMOSPHERE 2015; 118:239-245. [PMID: 25282689 DOI: 10.1016/j.chemosphere.2014.08.072] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2014] [Revised: 08/21/2014] [Accepted: 08/25/2014] [Indexed: 06/03/2023]
Abstract
In this work, we propose and analyze single-descriptor based quantitative structure-property relationships (QSPRs) developed using quantum-chemical descriptors and their electron-correlation contribution, for various physico-chemical properties, namely, aqueous solubility, subcooled liquid vapour pressure, n-octanol/water and n-octanol/air partition coefficients of polychlorinated-dibenzo-p-dioxins (PCDDs) and -dibenzo-furans (PCDFs). The predictivity of the developed QSPRs is examined through external validation procedures employing an external prediction set of chemicals not used in the QSPR model development. From the comparison of different models, it is observed that the total energy and the mean polarizability of a molecule are highly significant in determining the predictivity of a QSPR, whereas dipole moment is found to be a poor descriptor in the models developed for all the properties analyzed. Besides these, the electron density based descriptors, namely, absolute electronegativity and electrophilicity index, were also observed to be highly influential in determining the external prediction for partition coefficients.
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Sangwan P. Role of electron correlation in the polydeprotonation of benzene to form trianions. J PHYS ORG CHEM 2014. [DOI: 10.1002/poc.3302] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Affiliation(s)
- Vikas
- Quantum Chemistry Group, Department of Chemistry and Centre of Advanced Studies in Chemistry; Panjab University; Chandigrah 160014 India
| | - Poonam Sangwan
- Quantum Chemistry Group, Department of Chemistry and Centre of Advanced Studies in Chemistry; Panjab University; Chandigrah 160014 India
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Reenu, Vikas. Electron-correlation based externally predictive QSARs for mutagenicity of nitrated-PAHs in Salmonella typhimurium TA100. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2014; 101:42-50. [PMID: 24507125 DOI: 10.1016/j.ecoenv.2013.11.020] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2013] [Revised: 11/20/2013] [Accepted: 11/22/2013] [Indexed: 05/25/2023]
Abstract
In quantitative modeling, there are two major aspects that decide reliability and real external predictivity of a structure-activity relationship (SAR) based on quantum chemical descriptors. First, the information encoded in employed molecular descriptors, computed through a quantum-mechanical method, should be precisely estimated. The accuracy of the quantum-mechanical method, however, is dependent upon the amount of electron-correlation it incorporates. Second, the real external predictivity of a developed quantitative SAR (QSAR) should be validated employing an external prediction set. In this work, to analyze the role of electron-correlation, QSAR models are developed for a set of 51 ubiquitous pollutants, namely, nitrated monocyclic and polycyclic aromatic hydrocarbons (nitrated-AHs and PAHs) having mutagenic activity in TA100 strain of Salmonella typhimurium. The quality of the models, through state-of-the-art external validation procedures employing an external prediction set, is compared to the best models known in the literature for mutagenicity. The molecular descriptors whose electron-correlation contribution is analyzed include total energy, energy of HOMO and LUMO, and commonly employed electron-density based descriptors such as chemical hardness, chemical softness, absolute electronegativity and electrophilicity index. The electron-correlation based QSARs are also compared with those developed using quantum-mechanical descriptors computed with advanced semi-empirical (SE) methods such as PM6, PM7, RM1, and ab initio methods, namely, the Hartree-Fock (HF) and the density functional theory (DFT). The models, developed using electron-correlation contribution of the quantum-mechanical descriptors, are found to be not only reliable but also satisfactorily predictive when compared to the existing robust models. The robustness of the models based on descriptors computed through advanced SE methods, is also observed to be comparable to those developed with the electron-correlation based descriptors. The work emphasizes that the correlation-energy can serve as a reliable descriptor to explore the origin of biological activities at the level of electron-dynamics.
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Vikas, Chayawan. Externally predictive quantitative modeling of supercooled liquid vapor pressure of polychlorinated-naphthalenes through electron-correlation based quantum-mechanical descriptors. CHEMOSPHERE 2014; 95:448-454. [PMID: 24168755 DOI: 10.1016/j.chemosphere.2013.09.093] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2013] [Accepted: 09/21/2013] [Indexed: 06/02/2023]
Abstract
For predicting physico-chemical properties related to environmental fate of molecules, quantitative structure-property relationships (QSPRs) are valuable tools in environmental chemistry. For developing a QSPR, molecular descriptors computed through quantum-mechanical methods are generally employed. The accuracy of a quantum-mechanical method, however, rests on the amount of electron-correlation estimated by the method. In this work, single-descriptor QSPRs for supercooled liquid vapor pressure of chloronaphthalenes and polychlorinated-naphthalenes are developed using molecular descriptors based on the electron-correlation contribution of the quantum-mechanical descriptor. The quantum-mechanical descriptors for which the electron-correlation contribution is analyzed include total-energy, mean polarizability, dipole moment, frontier orbital (HOMO/LUMO) energy, and density-functional theory (DFT) based descriptors, namely, absolute electronegativity, chemical hardness, and electrophilicity index. A total of 40 single-descriptor QSPRs were developed using molecular descriptors computed with advanced semi-empirical (SE) methods, namely, RM1, PM7, and ab intio methods, namely, Hartree-Fock and DFT. The developed QSPRs are validated using state-of-the-art external validation procedures employing an external prediction set. From the comparison of external predictivity of the models, it is observed that the single-descriptor QSPRs developed using total energy and correlation energy are found to be far more robust and predictive than those developed using commonly employed descriptors such as HOMO/LUMO energy and dipole moment. The work proposes that if real external predictivity of a QSPR model is desired to be explored, particularly, in terms of intra-molecular interactions, correlation-energy serves as a more appropriate descriptor than the polarizability. However, for developing QSPRs, computationally inexpensive advanced SE methods such as PM7 can be more reliable than the expensive ab inito methods.
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