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Huang R, Liu H, Wei Z, Jiang Y, Pan K, Wang X, Kong J. Insights into the quantitative structure-activity relationship for ionic liquids: a bibliometric mapping analysis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:95054-95076. [PMID: 37581727 DOI: 10.1007/s11356-023-29285-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 08/07/2023] [Indexed: 08/16/2023]
Abstract
Environmental protection and sustainability is the development goal that countries all over the world are pursuing. Ionic liquids (ILs), as a new type of green material, have a great application prospect. And the quantitative structure-activity relationship (QSAR) is significant for the research of ILs. To better understand the role played by QSAR in the research of ILs, 4139 literatures published in the WOS database from 2002 to 2022 were used for bibliometric analysis, and different types of knowledge maps were mapped to obtain the current status and trends of IL research applied QSAR. The distribution pattern of the literature output chronology, country, institution, author cooperation, and major source journals can be obtained through the research of the distribution of literature. Through core literature, dual-map overlays, and evolutionary path analysis, the research knowledge base was obtained mainly including ionic liquid toxicological properties research, environmental protection and sustainability, ionic liquid design, and mild steel corrosion inhibition; through the co-occurrence and evolution of keywords, the current research hotspots are basic properties of ILs, corrosion inhibition of mild steel, the effect of toxicity on the environment, QSAR modeling methods, solvent application of ILs, and drug design.
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Affiliation(s)
- Rui Huang
- College of Quality and Safety Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Hui Liu
- College of Quality and Safety Engineering, China Jiliang University, Hangzhou, 310018, China.
- State Key Laboratory Cultivation Base for Gas Geology and Gas Control, Henan Polytechnic University, Jiaozuo, 454000, China.
| | - Ze Wei
- College of Quality and Safety Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Yi Jiang
- College of Quality and Safety Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Kai Pan
- College of Quality and Safety Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Xin Wang
- College of Quality and Safety Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Jie Kong
- College of Quality and Safety Engineering, China Jiliang University, Hangzhou, 310018, China
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Danieli A, Colombo E, Raitano G, Lombardo A, Roncaglioni A, Manganaro A, Sommovigo A, Carnesecchi E, Dorne JLCM, Benfenati E. The VEGA Tool to Check the Applicability Domain Gives Greater Confidence in the Prediction of In Silico Models. Int J Mol Sci 2023; 24:9894. [PMID: 37373049 PMCID: PMC10298077 DOI: 10.3390/ijms24129894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/31/2023] [Accepted: 06/03/2023] [Indexed: 06/29/2023] Open
Abstract
A sound assessment of in silico models and their applicability domain can support the use of new approach methodologies (NAMs) in chemical risk assessment and requires increasing the users' confidence in this approach. Several approaches have been proposed to evaluate the applicability domain of such models, but their prediction power still needs a thorough assessment. In this context, the VEGA tool capable of assessing the applicability domain of in silico models is examined for a range of toxicological endpoints. The VEGA tool evaluates chemical structures and other features related to the predicted endpoints and is efficient in measuring applicability domain, enabling the user to identify less accurate predictions. This is demonstrated with many models addressing different endpoints, towards toxicity of relevance to human health, ecotoxicological endpoints, environmental fate, physicochemical and toxicokinetic properties, for both regression models and classifiers.
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Affiliation(s)
- Alberto Danieli
- Department of Biotechnology and Life Science, University of Insubria, Via Dunant 3, 21100 Varese, Italy;
| | - Erika Colombo
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
| | - Giuseppa Raitano
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
| | - Anna Lombardo
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
| | - Alessandra Roncaglioni
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
| | | | | | - Edoardo Carnesecchi
- European Food Safety Authority (EFSA), Via Carlo Magno 1A, 43126 Parma, Italy; (E.C.); (J.-L.C.M.D.)
| | - Jean-Lou C. M. Dorne
- European Food Safety Authority (EFSA), Via Carlo Magno 1A, 43126 Parma, Italy; (E.C.); (J.-L.C.M.D.)
| | - Emilio Benfenati
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
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Zhou Z, Eden M, Shen W. Treat Molecular Linear Notations as Sentences: Accurate Quantitative Structure–Property Relationship Modeling via a Natural Language Processing Approach. Ind Eng Chem Res 2023. [DOI: 10.1021/acs.iecr.2c04070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
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Kumar P, Singh R, Kumar A, Toropova AP, Toropov AA, Devi M, Lal S, Sindhu J, Singh D. Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studies, their molecular docking and ADME analysis. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:677-700. [PMID: 36093620 DOI: 10.1080/1062936x.2022.2120068] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
The application of QSAR along with other in silico tools like molecular docking, and molecular dynamics provide a lot of promise for finding new treatments for life-threatening diseases like Type 2 diabetes mellitus (T2DM). The present study is an attempt to develop Monte Carlo algorithm-based QSAR models using freely available CORAL software. The experimental data on the α-amylase inhibition by a series of benzothiazole-linked hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids were selected as endpoint for the model generation. Initially, a total of eight QSAR models were built using correlation intensity index (CII) as a criterion of predictive potential. The model developed from split 6 using CII was the most reliable because of the highest numerical value of the determination coefficient of the validation set (r2VAL = 0.8739). The important structural fragments responsible for altering the endpoint were also extracted from the best-built model. With the goal of improved prediction quality and lower prediction errors, the validated models were used to build consensus models. Molecular docking was used to know the binding mode and pose of the selected derivatives. Further, to get insight into their metabolism by living beings, ADME studies were investigated using internet freeware, SwissADME.
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Affiliation(s)
- P Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - R Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - A Kumar
- Department of Pharmaceutical Sciences, GJUS&T, Hisar, India
| | - A P Toropova
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - A A Toropov
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - M Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - S Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - J Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
| | - D Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, India
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Lavado GJ, Baderna D, Carnesecchi E, Toropova AP, Toropov AA, Dorne JLCM, Benfenati E. QSAR models for soil ecotoxicity: Development and validation of models to predict reproductive toxicity of organic chemicals in the collembola Folsomia candida. JOURNAL OF HAZARDOUS MATERIALS 2022; 423:127236. [PMID: 34844354 DOI: 10.1016/j.jhazmat.2021.127236] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 09/09/2021] [Accepted: 09/13/2021] [Indexed: 06/13/2023]
Abstract
Soil pollution is a critical environmental challenge: the substances released in the soil can adversely affect humans and the ecosystem. Several bioassays were developed to investigate the soil ecotoxicity of chemicals with soil microbes, plants, invertebrates and vertebrates. The 28-day collembolan reproduction test with the springtail Folsomia candida is a recently introduced bioassay described by OECD guideline 232. Although the importance of springtails for maintaining soil quality, toxicity data for Collembola are still limited. We have developed two QSAR models for the prediction of reproductive toxicity induced by organic compounds in Folsomia candida using 28 days NOEC data. We assembled a dataset with the highest number of compounds available so far: 54 compounds were collected from publicly available sources, including plant protection products, reactive intermediates and industrial chemicals, household and cosmetic ingredients, drugs, environmental transformation products and polycyclic aromatic hydrocarbons. The models were developed using partial least squares regression (PLS) and the Monte Carlo technique with respectively the open source tools Small Dataset Modeler and CORAL software. Both QSAR models gave good predictive performance even though based on a small dataset, so they could serve for the ecological risk assessment of chemicals for terrestrial organisms.
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Affiliation(s)
- Giovanna J Lavado
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Sciences Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milano, Italy
| | - Diego Baderna
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Sciences Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milano, Italy.
| | - Edoardo Carnesecchi
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands
| | - Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Sciences Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milano, Italy
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Sciences Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milano, Italy
| | - Jean Lou C M Dorne
- Scientific Committee and Emerging Risks Unit, European Food Safety Authority, Via Carlo Magno 1A, Parma, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Sciences Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milano, Italy
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