133 results on '"Maran U"'
Search Results
2. P05-01 Ensuring in silico models for toxicology are FAIR
- Author
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Cronin, M.T., Basiri, H., Belfield, S.J., Enoch, S.J., Firman, J.W., Hardy, B., Madden, J.C., Maran, U., Piir, G., and Sild, S.
- Published
- 2024
- Full Text
- View/download PDF
3. ELIXIR and Toxicology: a community in development [version 2; peer review: 2 approved]
- Author
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Martens, M., Stierum, R., Schymanski, E.L., Evelo, C.T., Aalizadeh, R., Aladjov, H., Arturi, K., Audouze, K., Babica, P., Berka, K., Bessems, J., Blaha, L., Bolton, E.E., Cases, M., Damalas, D.E., Dave, K., Dilger, M., Exner, T., Geerke, D.P., Grafström, R., Gray, A., Hancock, J.M., Hollert, H., Jeliazkova, N., Jennen, D., Jourdan, F., Kahlem, P., Klanova, J., Kleinjans, J., Kondic, T., Kone, B., Lynch, I., Maran, U., Martinez Cuesta, S., Ménager, H., Neumann, S., Nymark, P., Oberacher, H., Ramirez, N., Remy, S., Rocca-Serra, P., Salek, R.M., Sallach, B., Sansone, S.-A., Sanz, F., Sarimveis, H., Sarntivijai, S., Schulze, Tobias, Slobodnik, J., Spjuth, O., Tedds, J., Thomaidis, N., Weber, R.J.M., van Westen, G.J.P., Wheelock, C.E., Williams, A.J., Witters, H., Zdrazil, B., Županič, A., Willighagen, E.L., Martens, M., Stierum, R., Schymanski, E.L., Evelo, C.T., Aalizadeh, R., Aladjov, H., Arturi, K., Audouze, K., Babica, P., Berka, K., Bessems, J., Blaha, L., Bolton, E.E., Cases, M., Damalas, D.E., Dave, K., Dilger, M., Exner, T., Geerke, D.P., Grafström, R., Gray, A., Hancock, J.M., Hollert, H., Jeliazkova, N., Jennen, D., Jourdan, F., Kahlem, P., Klanova, J., Kleinjans, J., Kondic, T., Kone, B., Lynch, I., Maran, U., Martinez Cuesta, S., Ménager, H., Neumann, S., Nymark, P., Oberacher, H., Ramirez, N., Remy, S., Rocca-Serra, P., Salek, R.M., Sallach, B., Sansone, S.-A., Sanz, F., Sarimveis, H., Sarntivijai, S., Schulze, Tobias, Slobodnik, J., Spjuth, O., Tedds, J., Thomaidis, N., Weber, R.J.M., van Westen, G.J.P., Wheelock, C.E., Williams, A.J., Witters, H., Zdrazil, B., Županič, A., and Willighagen, E.L.
- Abstract
Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.
- Published
- 2021
4. In silico characteristics of structure and activity of the food-derived peptides with an ACE inhibitory bioactivity: SW06.W29–4
- Author
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Iwaniak, A., Maran, U., Darewicz, M., and Minkiewicz, P.
- Published
- 2013
5. Molecular Descriptors from Two-Dimensional Chemical Structure
- Author
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Maran, U., primary, Sild, S., additional, Tulp, I., additional, Takkis, K., additional, and Moosus, M., additional
- Published
- 2010
- Full Text
- View/download PDF
6. Chapter 6. Molecular Descriptors from Two-Dimensional Chemical Structure
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Maran, U., primary, Sild, S., additional, Tulp, I., additional, Takkis, K., additional, and Moosus, M., additional
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- 2010
- Full Text
- View/download PDF
7. Modelling of antiproliferative activity measured in HeLa cervical cancer cells in a series of xanthene derivatives
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Zukić, S., primary and Maran, U., additional
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- 2020
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8. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity
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Mansouri, K, Kleinstreuer, N, Abdelaziz, A, Alberga, D, Alves, V, Andersson, P, Andrade, C, Bai, F, Balabin, I, Ballabio, D, Benfenati, E, Bhhatarai, B, Boyer, S, Chen, J, Consonni, V, Farag, S, Fourches, D, García-Sosa, A, Gramatica, P, Grisoni, F, Grulke, C, Hong, H, Horvath, D, Hu, X, Huang, R, Jeliazkova, N, Li, J, Li, X, Liu, H, Manganelli, S, Mangiatordi, G, Maran, U, Marcou, G, Martin, T, Muratov, E, Nguyen, D, Nicolotti, O, Nikolov, N, Norinder, U, Papa, E, Petitjean, M, Piir, G, Pogodin, P, Poroikov, V, Qiao, X, Richard, A, Roncaglioni, A, Ruiz, P, Rupakheti, C, Sakkiah, S, Sangion, A, Schramm, K, Selvaraj, C, Shah, I, Sild, S, Sun, L, Taboureau, O, Tang, Y, Tetko, I, Todeschini, R, Tong, W, Trisciuzzi, D, Tropsha, A, Van Den Driessche, G, Varnek, A, Wang, Z, Wedebye, E, Williams, A, Xie, H, Zakharov, A, Zheng, Z, Judson, R, Mansouri, Kamel, Kleinstreuer, Nicole, Abdelaziz, Ahmed M., Alberga, Domenico, Alves, Vinicius M., Andersson, Patrik L., Andrade, Carolina H., Bai, Fang, Balabin, Ilya, Ballabio, Davide, Benfenati, Emilio, Bhhatarai, Barun, Boyer, Scott, Chen, Jingwen, Consonni, Viviana, Farag, Sherif, Fourches, Denis, García-Sosa, Alfonso T., Gramatica, Paola, Grisoni, Francesca, Grulke, Chris M., Hong, Huixiao, Horvath, Dragos, Hu, Xin, Huang, Ruili, Jeliazkova, Nina, Li, Jiazhong, Li, Xuehua, Liu, Huanxiang, Manganelli, Serena, Mangiatordi, Giuseppe F., Maran, Uko, Marcou, Gilles, Martin, Todd, Muratov, Eugene, Nguyen, Dac-Trung, Nicolotti, Orazio, Nikolov, Nikolai G., Norinder, Ulf, Papa, Ester, Petitjean, Michel, Piir, Geven, Pogodin, Pavel, Poroikov, Vladimir, Qiao, Xianliang, Richard, Ann M., Roncaglioni, Alessandra, Ruiz, Patricia, Rupakheti, Chetan, Sakkiah, Sugunadevi, Sangion, Alessandro, Schramm, Karl-Werner, Selvaraj, Chandrabose, Shah, Imran, Sild, Sulev, Sun, Lixia, Taboureau, Olivier, Tang, Yun, Tetko, Igor V., Todeschini, Roberto, Tong, Weida, Trisciuzzi, Daniela, Tropsha, Alexander, Van Den Driessche, George, Varnek, Alexandre, Wang, Zhongyu, Wedebye, Eva B., Williams, Antony J., Xie, Hongbin, Zakharov, Alexey V., Zheng, Ziye, Judson, Richard S., Mansouri, K, Kleinstreuer, N, Abdelaziz, A, Alberga, D, Alves, V, Andersson, P, Andrade, C, Bai, F, Balabin, I, Ballabio, D, Benfenati, E, Bhhatarai, B, Boyer, S, Chen, J, Consonni, V, Farag, S, Fourches, D, García-Sosa, A, Gramatica, P, Grisoni, F, Grulke, C, Hong, H, Horvath, D, Hu, X, Huang, R, Jeliazkova, N, Li, J, Li, X, Liu, H, Manganelli, S, Mangiatordi, G, Maran, U, Marcou, G, Martin, T, Muratov, E, Nguyen, D, Nicolotti, O, Nikolov, N, Norinder, U, Papa, E, Petitjean, M, Piir, G, Pogodin, P, Poroikov, V, Qiao, X, Richard, A, Roncaglioni, A, Ruiz, P, Rupakheti, C, Sakkiah, S, Sangion, A, Schramm, K, Selvaraj, C, Shah, I, Sild, S, Sun, L, Taboureau, O, Tang, Y, Tetko, I, Todeschini, R, Tong, W, Trisciuzzi, D, Tropsha, A, Van Den Driessche, G, Varnek, A, Wang, Z, Wedebye, E, Williams, A, Xie, H, Zakharov, A, Zheng, Z, Judson, R, Mansouri, Kamel, Kleinstreuer, Nicole, Abdelaziz, Ahmed M., Alberga, Domenico, Alves, Vinicius M., Andersson, Patrik L., Andrade, Carolina H., Bai, Fang, Balabin, Ilya, Ballabio, Davide, Benfenati, Emilio, Bhhatarai, Barun, Boyer, Scott, Chen, Jingwen, Consonni, Viviana, Farag, Sherif, Fourches, Denis, García-Sosa, Alfonso T., Gramatica, Paola, Grisoni, Francesca, Grulke, Chris M., Hong, Huixiao, Horvath, Dragos, Hu, Xin, Huang, Ruili, Jeliazkova, Nina, Li, Jiazhong, Li, Xuehua, Liu, Huanxiang, Manganelli, Serena, Mangiatordi, Giuseppe F., Maran, Uko, Marcou, Gilles, Martin, Todd, Muratov, Eugene, Nguyen, Dac-Trung, Nicolotti, Orazio, Nikolov, Nikolai G., Norinder, Ulf, Papa, Ester, Petitjean, Michel, Piir, Geven, Pogodin, Pavel, Poroikov, Vladimir, Qiao, Xianliang, Richard, Ann M., Roncaglioni, Alessandra, Ruiz, Patricia, Rupakheti, Chetan, Sakkiah, Sugunadevi, Sangion, Alessandro, Schramm, Karl-Werner, Selvaraj, Chandrabose, Shah, Imran, Sild, Sulev, Sun, Lixia, Taboureau, Olivier, Tang, Yun, Tetko, Igor V., Todeschini, Roberto, Tong, Weida, Trisciuzzi, Daniela, Tropsha, Alexander, Van Den Driessche, George, Varnek, Alexandre, Wang, Zhongyu, Wedebye, Eva B., Williams, Antony J., Xie, Hongbin, Zakharov, Alexey V., Zheng, Ziye, and Judson, Richard S.
- Abstract
BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP’s list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCastTM metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCastTM/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy ass
- Published
- 2020
9. QSAR modeling and chemical space analysis of antimalarial compounds
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Sidorov P., Viira B., Davioud-Charvet E., Maran U., Marcou G., Horvath D., and Varnek A.
- Subjects
Antimalarial compounds ,Quantitative structure–activity relationships (QSAR) ,Mode of action ,Chemical space ,Generative topographic mapping (GTM) - Abstract
© 2017, Springer International Publishing Switzerland.Generative topographic mapping (GTM) has been used to visualize and analyze the chemical space of antimalarial compounds as well as to build predictive models linking structure of molecules with their antimalarial activity. For this, a database, including ~3000 molecules tested in one or several of 17 anti-Plasmodium activity assessment protocols, has been compiled by assembling experimental data from in-house and ChEMBL databases. GTM classification models built on subsets corresponding to individual bioassays perform similarly to the earlier reported SVM models. Zones preferentially populated by active and inactive molecules, respectively, clearly emerge in the class landscapes supported by the GTM model. Their analysis resulted in identification of privileged structural motifs of potential antimalarial compounds. Projection of marketed antimalarial drugs on this map allowed us to delineate several areas in the chemical space corresponding to different mechanisms of antimalarial activity. This helped us to make a suggestion about the mode of action of the molecules populating these zones.
- Published
- 2017
10. QSAR modeling1. and chemical space analysis of antimalarial compounds
- Author
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Sidorov, Pavel, Viira, B, Davioud-charvet, Elisabeth, Maran, U, Marcou, Gilles, Horvath, Dragos, Varnek, Alexandre, Laboratoire de Chimie de Coordination Organique, Tectonique Moléculaire du Solide (TMS), Chimie de la matière complexe (CMC), Université de Strasbourg (UNISTRA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Université de Strasbourg (UNISTRA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'innovation moléculaire et applications (LIMA), and Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
CBM ,[CHIM.ORGA]Chemical Sciences/Organic chemistry - Published
- 2017
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11. Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development
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Tetko I., Maran U., and Tropsha A.
- Subjects
model repositories ,QSAR ,QSPR ,web-based models ,on-line modeling environments ,chemoinformatics - Abstract
© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, WeinheimThousands of (Quantitative) Structure-Activity Relationships (Q)SAR models have been described in peer-reviewed publications; however, this way of sharing seldom makes models available for the use by the research community outside of the developer's laboratory. Conversely, on-line models allow broad dissemination and application representing the most effective way of sharing the scientific knowledge. Approaches for sharing and providing on-line access to models range from web services created by individual users and laboratories to integrated modeling environments and model repositories. This emerging transition from the descriptive and informative, but “static”, and for the most part, non-executable print format to interactive, transparent and functional delivery of “living” models is expected to have a transformative effect on modern experimental research in areas of scientific and regulatory use of (Q)SAR models.
- Published
- 2017
12. Quantitative structure–permeability relationships at various pH values for acidic and basic drugs and drug-like compounds
- Author
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Oja, M., primary and Maran, U., additional
- Published
- 2015
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13. UNICORE – a middleware for Life Sciences grids
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Bala, P, Baldridge, K K, Benfenati, E, Casalegno, M, Maran, U, Miroslaw, L, Ostropytskyy, V, Rasch, K, Sild, S, Schöne, R, Williams, N, University of Zurich, and Cannataro, M
- Subjects
10120 Department of Chemistry ,540 Chemistry - Published
- 2009
14. Classifying bio-concentration factor with random forest algorithm, influence of the bio-accumulative vs. non-bio-accumulative compound ratio to modelling result, and applicability domain for random forest model
- Author
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Piir, G., primary, Sild, S., additional, and Maran, U., additional
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- 2014
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15. UNICORE – a middleware for Life Sciences grids
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Cannataro, M, Cannataro, M ( M ), Bala, P, Baldridge, K K, Benfenati, E, Casalegno, M, Maran, U, Miroslaw, L, Ostropytskyy, V, Rasch, K, Sild, S, Schöne, R, Williams, N, Cannataro, M, Cannataro, M ( M ), Bala, P, Baldridge, K K, Benfenati, E, Casalegno, M, Maran, U, Miroslaw, L, Ostropytskyy, V, Rasch, K, Sild, S, Schöne, R, and Williams, N
- Abstract
The Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine, and Healthcare brings together state-of-the art methodologies and developments of grid technologies applied in different fields of life sciences. This Handbook of Research considers the use of grid technologies to support research and application of each information level where life science research takes place - a useful reference source for academicians, medical practitioners, and researchers involved in all areas of healthcare technologies.
- Published
- 2009
16. Drugs, non-drugs, and disease category specificity: organ effects by ligand pharmacology1
- Author
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García-Sosa, A.T., primary and Maran, U., additional
- Published
- 2013
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17. Comparative analysis of local and consensus quantitative structure-activity relationship approaches for the prediction of bioconcentration factor
- Author
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Piir, G., primary, Sild, S., additional, and Maran, U., additional
- Published
- 2013
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18. Description of the electronic structure of organic chemicals using semiempirical and ab initio methods for development of toxicological QSARs
- Author
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Netzeva, T.I., Aptula, Aynur, Benfenati, E., Cronin, M.T.D., Gini, G., Lessigiarska, I., Maran, U., Vračko, M., Schüürmann, Gerrit, Netzeva, T.I., Aptula, Aynur, Benfenati, E., Cronin, M.T.D., Gini, G., Lessigiarska, I., Maran, U., Vračko, M., and Schüürmann, Gerrit
- Abstract
The quality of quantitative structure−activity relationship (QSAR) models depends on the quality of their constitutive elements including the biological activity, statistical procedure applied, and the physicochemical and structural descriptors. The aim of this study was to assess the comparative use of ab initio and semiempirical quantum chemical calculations for the development of toxicological QSARs applied to a large and chemically diverse data set. A heterogeneous collection of 568 organic compounds with 96 h acute toxicity measured to the fish fathead minnow (Pimephales promelas) was utilized. A total of 162 descriptors were calculated using the semiempirical AM1 Hamiltonian, and 121 descriptors were compiled using an ab initio (B3LYP/6-31G**) method. The QSARs were derived using multiple linear regression (MLR) and partial least squares (PLS) analyses. Statistically similar models were obtained using AM1 and B3LYP calculated descriptors supported by the use of the logarithm of the octanol−water partition coefficient (log Kow). The main difference between the models derived by both MLR and PLS with the two sets of quantum chemical descriptors was concentrated on the type of descriptors selected. It was concluded that for large-scale predictions, irrespective of the mechanism of toxic action, the use of precise but time-consuming ab initio methods does not offer considerable advantage compared to the semiempirical calculations and could be avoided.
- Published
- 2005
19. Molecular Property Filters Describing Pharmacokinetics and Drug Binding
- Author
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T. Garcia-Sosa, A., primary, Maran, U., additional, and Hetenyi, C., additional
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- 2012
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20. Quantitative structure–activity relationship analysis of acute toxicity of diverse chemicals to Daphnia magna with whole molecule descriptors
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Moosus, M., primary and Maran, U., additional
- Published
- 2011
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21. QSAR model for the prediction of bio-concentration factor using aqueous solubility and descriptors considering various electronic effects
- Author
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Piir, G., primary, Sild, S., additional, Roncaglioni, A., additional, Benfenati, E., additional, and Maran, U., additional
- Published
- 2010
- Full Text
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22. ChemInform Abstract: Theoretical Study of the Keto-Enol Tautomerism in Aqueous Solutions.
- Author
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KARELSON, M., primary, MARAN, U., additional, and KATRITZKY, A. R., additional
- Published
- 2010
- Full Text
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23. Non-Linear QSAR Treatment of Genotoxicity
- Author
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Karelson, M., primary, Sild, S., additional, and Maran, U., additional
- Published
- 2000
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24. Description of the Electronic Structure of Organic Chemicals Using Semiempirical and Ab Initio Methods for Development of Toxicological QSARs
- Author
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Netzeva, T. I., Aptula, A. O., Benfenati, E., Cronin, M. T. D., Gini, G., Lessigiarska, I., Maran, U., Vracko, M., and Schuurmann, G.
- Abstract
The quality of quantitative structure−activity relationship (QSAR) models depends on the quality of their constitutive elements including the biological activity, statistical procedure applied, and the physicochemical and structural descriptors. The aim of this study was to assess the comparative use of ab initio and semiempirical quantum chemical calculations for the development of toxicological QSARs applied to a large and chemically diverse data set. A heterogeneous collection of 568 organic compounds with 96 h acute toxicity measured to the fish fathead minnow (Pimephales promelas) was utilized. A total of 162 descriptors were calculated using the semiempirical AM1 Hamiltonian, and 121 descriptors were compiled using an ab initio (B3LYP/6-31G**) method. The QSARs were derived using multiple linear regression (MLR) and partial least squares (PLS) analyses. Statistically similar models were obtained using AM1 and B3LYP calculated descriptors supported by the use of the logarithm of the octanol−water partition coefficient (log K
ow ). The main difference between the models derived by both MLR and PLS with the two sets of quantum chemical descriptors was concentrated on the type of descriptors selected. It was concluded that for large-scale predictions, irrespective of the mechanism of toxic action, the use of precise but time-consuming ab initio methods does not offer considerable advantage compared to the semiempirical calculations and could be avoided.- Published
- 2005
25. QSPR Treatment of the Soil Sorption Coefficients of Organic Pollutants
- Author
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Kahn, I., Fara, D., Karelson, M., Maran, U., and Andersson, P. L.
- Abstract
In this study, general and class-specific QSPR models for soil sorption, logK
OC , of 344 organic pollutants (0 < logK OC < 4.94) were developed using a large variety of theoretical molecular descriptors based only on molecular structure. Two general models were obtained. The first model was derived for a structurally representative set of 68 chemicals (R2=0.76, s=0.44), whereas the second involved a total of 344 compounds (R2=0.76, s=0.41). The first was validated using the data for the remaining 276 pollutants (R2=0.70, s=0.45). An additional validation of both models was performed using an independent set of 48 pollutants. Both models predict the logK OC at the level of experimental precision, while the theoretical molecular descriptors appearing in the QSPR models give further insight into the mechanisms of soil sorption. The analysis of the distribution of the residuals of the logK OC values calculated by both general models indicated the need and possible advantages of modeling soil sorption for smaller data sets related to individual classes of chemicals. Accordingly, QSPR models were also developed for 14 chemical classes. The descriptors appearing in these models were discussed as related to the possible interaction mechanisms in soil sorption. - Published
- 2005
26. General and Class Specific Models for Prediction of Soil Sorption Using Various Physicochemical Descriptors
- Author
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Andersson, P. L., Maran, U., Fara, D., Karelson, M., and Hermens, J. L. M.
- Abstract
Diverse chemical descriptors were explored for use in QSAR models aimed to screen the soil sorption potential of organic compounds. The descriptors included logP, HyperChem QSARProperties descriptors, a combination of connectivity indices, geometrical, and quantum chemical measures, and two sets from the DRAGON and CODESSA program packages, respectively. Generally, the univariate logP models were capable of capturing most of the variation and give an indication of the sorption potential. The multivariate models required refined variable selection procedures but were shown to include crucial descriptors for modeling compound classes with specific chemical characteristics.
- Published
- 2002
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27. Theoretical Descriptors for the Correlation of Aquatic Toxicity of Environmental Pollutants by Quantitative Structure-Toxicity Relationships
- Author
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Katritzky, A. R., Tatham, D. B., and Maran, U.
- Abstract
Quantitative structure-toxicity relationships were developed for the prediction of aqueous toxicities for Poecilia reticulata (guppy) using the CODESSA treatment. A two-parameter correlation was found for class 1 toxins with R2 = 0.96, and a five-parameter correlation was found for class 2 toxins with R2 = 0.92. A five-parameter correlation for class 3 toxins had R2 = 0.85. The correlations for class 4 toxins were less satisfactory. All the descriptors utilized are calculated solely from the structures of the molecules, which makes it possible to predict unavailable or unknown toxins.
- Published
- 2001
28. Perspective on the Relationship between Melting Points and Chemical Structure
- Author
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Katritzky, A. R., Jain, R., Lomaka, A., Petrukhin, R., Maran, U., and Karelson, M.
- Abstract
The importance of melting points in characterization, in the estimation of other physical properties and toxicity, and in practical applications such as ionic liquids is summarized, as are difficulties in the systematic treatment of melting points in terms of QSPR. Classical correlations of melting points of congeneric and diverse sets are discussed together with group contribution methods, combined approaches, and computer simulations.
- Published
- 2001
29. Interpretation of Quantitative Structure−Property and −Activity Relationships
- Author
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Katritzky, A. R., Petrukhin, R., Tatham, D., Basak, S., Benfenati, E., Karelson, M., and Maran, U.
- Abstract
The potential utility of data reduction methods (e.g. principal component analysis) for the analysis of matrices assembled from the related properties of large sets of compounds is discussed by reference to results obtained from solvent polarity scales, ongoing work on solubilities and sweetness properties, and proposed general treatments of toxicities and gas chromatographic retention indices.
- Published
- 2001
30. Correlation of the Solubilities of Gases and Vapors in Methanol and Ethanol with Their Molecular Structures
- Author
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Katritzky, A. R., Tatham, D. B., and Maran, U.
- Abstract
Two four-parameter quantitative structure−property relations, with R2 = 0.95 and R2 = 0.97, respectively, gave good correlations for the solubilities of 87 gases and vapors in methanol and 61 in ethanol. All the descriptors used are derived solely from the structures of the molecules, making it possible to predict solubilities for unavailable or unknown compounds.
- Published
- 2001
31. Structurally Diverse Quantitative Structure−Property Relationship Correlations of Technologically Relevant Physical Properties
- Author
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Katritzky, A. R., Maran, U., Lobanov, V. S., and Karelson, M.
- Published
- 2000
32. Prediction of Melting Points for the Substituted Benzenes: A QSPR Approach
- Author
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Katritzky, A. R., Maran, U., Karelson, M., and Lobanov, V. S.
- Abstract
Quantitative structure−property relationships on a large set of descriptors are developed for the melting points of a large set of mono- and disubstituted benzenes (443 compounds). A correlation equation including nine descriptors (R2 = 0.8373) is reported for the whole set of compounds, and six descriptor equations are given for the subsets of ortho-, meta-, and para-substituted compounds, respectively. The importance of the hydrogen bonding descriptor (HDSA
2 ) is demonstrated, and quantum chemical descriptors are successfully applied to obtain predictive models. - Published
- 1997
33. New RAC1 inhibitors as potential pharmacological agents for heart failure treatment
- Author
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nicola ferri, Corsini, A., Ruggiero, C., Hoegberg, M., Tong, W., Robitzki, A. A., Maran, U., Sild, S., Garcia Sosa, A. T., Batzl Hartmann, C., Bartholomae, P., Uusitalo, J. S., Tolonen, A. T., Rousu, T., Hokkanen, J. K., and Turpeinen, M.
34. In silico characteristics of structure and activity of the food-derived peptides with an ACE inhibitory bioactivity
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Iwaniak, A., Maran, U., Darewicz, M., and Piotr Minkiewicz
35. Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development
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Tetko I., Maran U., Tropsha A., Tetko I., Maran U., and Tropsha A.
- Abstract
© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, WeinheimThousands of (Quantitative) Structure-Activity Relationships (Q)SAR models have been described in peer-reviewed publications; however, this way of sharing seldom makes models available for the use by the research community outside of the developer's laboratory. Conversely, on-line models allow broad dissemination and application representing the most effective way of sharing the scientific knowledge. Approaches for sharing and providing on-line access to models range from web services created by individual users and laboratories to integrated modeling environments and model repositories. This emerging transition from the descriptive and informative, but “static”, and for the most part, non-executable print format to interactive, transparent and functional delivery of “living” models is expected to have a transformative effect on modern experimental research in areas of scientific and regulatory use of (Q)SAR models.
36. QSAR modeling and chemical space analysis of antimalarial compounds
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Sidorov P., Viira B., Davioud-Charvet E., Maran U., Marcou G., Horvath D., Varnek A., Sidorov P., Viira B., Davioud-Charvet E., Maran U., Marcou G., Horvath D., and Varnek A.
- Abstract
© 2017, Springer International Publishing Switzerland.Generative topographic mapping (GTM) has been used to visualize and analyze the chemical space of antimalarial compounds as well as to build predictive models linking structure of molecules with their antimalarial activity. For this, a database, including ~3000 molecules tested in one or several of 17 anti-Plasmodium activity assessment protocols, has been compiled by assembling experimental data from in-house and ChEMBL databases. GTM classification models built on subsets corresponding to individual bioassays perform similarly to the earlier reported SVM models. Zones preferentially populated by active and inactive molecules, respectively, clearly emerge in the class landscapes supported by the GTM model. Their analysis resulted in identification of privileged structural motifs of potential antimalarial compounds. Projection of marketed antimalarial drugs on this map allowed us to delineate several areas in the chemical space corresponding to different mechanisms of antimalarial activity. This helped us to make a suggestion about the mode of action of the molecules populating these zones.
37. Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development
- Author
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Tetko I., Maran U., Tropsha A., Tetko I., Maran U., and Tropsha A.
- Abstract
© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, WeinheimThousands of (Quantitative) Structure-Activity Relationships (Q)SAR models have been described in peer-reviewed publications; however, this way of sharing seldom makes models available for the use by the research community outside of the developer's laboratory. Conversely, on-line models allow broad dissemination and application representing the most effective way of sharing the scientific knowledge. Approaches for sharing and providing on-line access to models range from web services created by individual users and laboratories to integrated modeling environments and model repositories. This emerging transition from the descriptive and informative, but “static”, and for the most part, non-executable print format to interactive, transparent and functional delivery of “living” models is expected to have a transformative effect on modern experimental research in areas of scientific and regulatory use of (Q)SAR models.
38. QSAR modeling and chemical space analysis of antimalarial compounds
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Sidorov P., Viira B., Davioud-Charvet E., Maran U., Marcou G., Horvath D., Varnek A., Sidorov P., Viira B., Davioud-Charvet E., Maran U., Marcou G., Horvath D., and Varnek A.
- Abstract
© 2017, Springer International Publishing Switzerland.Generative topographic mapping (GTM) has been used to visualize and analyze the chemical space of antimalarial compounds as well as to build predictive models linking structure of molecules with their antimalarial activity. For this, a database, including ~3000 molecules tested in one or several of 17 anti-Plasmodium activity assessment protocols, has been compiled by assembling experimental data from in-house and ChEMBL databases. GTM classification models built on subsets corresponding to individual bioassays perform similarly to the earlier reported SVM models. Zones preferentially populated by active and inactive molecules, respectively, clearly emerge in the class landscapes supported by the GTM model. Their analysis resulted in identification of privileged structural motifs of potential antimalarial compounds. Projection of marketed antimalarial drugs on this map allowed us to delineate several areas in the chemical space corresponding to different mechanisms of antimalarial activity. This helped us to make a suggestion about the mode of action of the molecules populating these zones.
39. ChemInform Abstract: Theoretical Study of the Keto-Enol Tautomerism in Aqueous Solutions.
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KARELSON, M., MARAN, U., and KATRITZKY, A. R.
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- 1997
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40. 183 Prediction of acute aquatic toxicity to fish comparing different QSAR approaches
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Roncaglioni, A., Colombo, A., Maran, U., karelson, M., and Benfenati, E.
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- 2003
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41. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity
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Geven Piir, Paola Gramatica, Vinicius M. Alves, Uko Maran, Xianliang Qiao, Michel Petitjean, Christopher M. Grulke, Karl-Werner Schramm, Giuseppe Felice Mangiatordi, Antony J. Williams, Barun Bhhatarai, Sherif Farag, Alexey V. Zakharov, Chetan Rupakheti, Emilio Benfenati, Weida Tong, Chandrabose Selvaraj, Eva Bay Wedebye, Fang Bai, Sugunadevi Sakkiah, Nicole Kleinstreuer, Ann M. Richard, Orazio Nicolotti, Huixiao Hong, George Van Den Driessche, Ilya A. Balabin, Eugene N. Muratov, Imran Shah, Ulf Norinder, Jiazhong Li, Huanxiang Liu, Viviana Consonni, Igor V. Tetko, Pavel V. Pogodin, Ruili Huang, Ester Papa, Ahmed Abdelaziz, Dragos Horvath, Alexandre Varnek, Patricia Ruiz, Carolina Horta Andrade, Domenico Alberga, Ziye Zheng, Roberto Todeschini, Daniela Trisciuzzi, Nina Jeliazkova, Xuehua Li, Dac-Trung Nguyen, Gilles Marcou, Zhongyu Wang, Kamel Mansouri, Alexander Tropsha, Yun Tang, Alessandro Sangion, Todd M. Martin, Xin Hu, Scott Boyer, Hongbin Xie, Serena Manganelli, Richard S. Judson, Nikolai Georgiev Nikolov, Jingwen Chen, Denis Fourches, Vladimir Poroikov, Alfonso T. García-Sosa, Alessandra Roncaglioni, Davide Ballabio, Patrik L. Andersson, Sulev Sild, Francesca Grisoni, Lixia Sun, Olivier Taboureau, US Environmental Protection Agency (EPA), University of Insubria, Varese, Laboratoire de Chémoinformatique, Chimie de la matière complexe (CMC), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Università degli studi di Bari, Unité de Biologie Fonctionnelle et Adaptative (BFA (UMR_8251 / U1133)), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), Mansouri, K, Kleinstreuer, N, Abdelaziz, A, Alberga, D, Alves, V, Andersson, P, Andrade, C, Bai, F, Balabin, I, Ballabio, D, Benfenati, E, Bhhatarai, B, Boyer, S, Chen, J, Consonni, V, Farag, S, Fourches, D, García-Sosa, A, Gramatica, P, Grisoni, F, Grulke, C, Hong, H, Horvath, D, Hu, X, Huang, R, Jeliazkova, N, Li, J, Li, X, Liu, H, Manganelli, S, Mangiatordi, G, Maran, U, Marcou, G, Martin, T, Muratov, E, Nguyen, D, Nicolotti, O, Nikolov, N, Norinder, U, Papa, E, Petitjean, M, Piir, G, Pogodin, P, Poroikov, V, Qiao, X, Richard, A, Roncaglioni, A, Ruiz, P, Rupakheti, C, Sakkiah, S, Sangion, A, Schramm, K, Selvaraj, C, Shah, I, Sild, S, Sun, L, Taboureau, O, Tang, Y, Tetko, I, Todeschini, R, Tong, W, Trisciuzzi, D, Tropsha, A, Van Den Driessche, G, Varnek, A, Wang, Z, Wedebye, E, Williams, A, Xie, H, Zakharov, A, Zheng, Z, Judson, R, National Institute of Environmental Health Sciences, National Institutes of Health, University of Bari Aldo Moro (UNIBA), Federal University of Goiás [Jataí], Dept. of Statistics - University of North Carolina - Chapel Hill, University of North Carolina System (UNC)-University of North Carolina System (UNC), Umeå University, Lanzhou University, Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Istituto di Ricerche Farmacologiche 'Mario Negri', Karolinska Institutet [Stockholm], Dalian University of Technology, Department of Statistics University of Milano Bicocca, University of North Carolina at Chapel Hill (UNC), North Carolina State University [Raleigh] (NC State), Institute of Computer Science [University of Tartu, Estonie], University of Tartu, U.S. ENVIRONMENTAL PROTECTION AGENCY USA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), U.S. Food and Drug Administration (FDA), Université de Strasbourg (UNISTRA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), National Institutes of Health [Bethesda] (NIH), Università degli studi di Bari Aldo Moro (UNIBA), Technical University of Denmark [Lyngby] (DTU), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Institute of Biomedical Chemistry [Moscou] (IBMC), Centers for Disease Control and Prevention, The University of Chicago Medicine [Chicago], East China University of Science and Technology, and German Research Center for Environmental Health - Helmholtz Center München (GmbH)
- Subjects
Databases, Factual ,Health, Toxicology and Mutagenesis ,Computational biology ,Pharmacology and Toxicology ,010501 environmental sciences ,Biology ,Endocrine Disruptors ,01 natural sciences ,03 medical and health sciences ,chemistry.chemical_compound ,0302 clinical medicine ,CHIM/01 - CHIMICA ANALITICA ,High-Throughput Screening Assays ,consensu ,Endocrine system ,Humans ,Computer Simulation ,030212 general & internal medicine ,United States Environmental Protection Agency ,0105 earth and related environmental sciences ,QSAR ,Research ,Public Health, Environmental and Occupational Health ,Farmakologi och toxikologi ,United States ,3. Good health ,Metabolic pathway ,machine learning ,chemistry ,13. Climate action ,Receptors, Androgen ,chemical modelling ,Androgen Receptor ,Androgens ,Xenobiotic ,Androgen receptor activity ,[CHIM.CHEM]Chemical Sciences/Cheminformatics ,Hormone - Abstract
Background:Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.Objectives:In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).Methods:The CoMPARA list of screened chemicals built on CERAPP’s list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.Results:The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.Discussion:The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program’s Integrated Chemical Environment. https://doi.org/10.1289/EHP5580
- Published
- 2020
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42. Predictive Modeling of Pesticides Reproductive Toxicity in Earthworms Using Interpretable Machine-Learning Techniques on Imbalanced Data.
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Kotli M, Piir G, and Maran U
- Abstract
The earthworm is a key indicator species in soil ecosystems. This makes the reproductive toxicity of chemical compounds to earthworms a desired property of determination and makes computational models necessary for descriptive and predictive purposes. Thus, the aim was to develop an advanced Quantitative Structure-Activity Relationship modeling approach for this complex property with imbalanced data. The approach integrated gradient-boosted decision trees as classifiers with a genetic algorithm for feature selection and Bayesian optimization for hyperparameter tuning. An additional goal was to analyze and interpret, using SHAP values, the structural features encoded by the molecular descriptors that contribute to pesticide toxicity and nontoxicity, the most notable of which are solvation entropy and a number of hydrolyzable bonds. The final model was constructed as a stacked ensemble of models and combined the strengths of the individual models. Evaluation of this model with an external test set of 147 compounds demonstrated a well-defined applicability domain and sufficient predictive capabilities with a Balanced Accuracy of 77%. The model representation follows FAIR principles and is available on QsarDB.org., Competing Interests: The authors declare no competing financial interest., (© 2025 The Authors. Published by American Chemical Society.)
- Published
- 2025
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43. Exploring the Influence of Ionic Liquid Anion Structure on Gas-Ionic Liquid Partition Coefficients of Organic Solutes Using Machine Learning.
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Toots KM, Sild S, Leis J, Acree WE, and Maran U
- Abstract
This article presents an in-depth investigation into the influence of anionic structures of ionic liquids (ILs) on gas-ionic liquid partition coefficients (log K ) of organic solutes in three ILs. While the primary objective was to examine whether there is a relationship between the molecular structure of the IL anion component and log K , additionally it was looked at whether the molecular descriptors of the anion in the relationships encode possible molecular interactions during the miscibility and partitioning in the IL. The research involves the compilation of data series of experimental log K values, where the cation component is constant. Such representative data series were obtained for three solutes─benzene, cyclohexane, and methanol─in three ILs with a uniform cationic component of methylimidazoliums. Using multiple linear regression models enhanced with machine learning techniques, the relationship between anionic structures and log K values was successfully quantified and modeled. Systematically selected molecular descriptors describing the anion structure show that in the case of methanol log K is strongly dependent on hydrogen bonds and Coulomb-dipolar interactions with the anion component, while in the case of benzene and cyclohexane the dispersion forces of the anion component are dominant. The outlier analysis and data interpretation highlight the need for extensive experimental data. The results confirm the initial hypothesis and provide valuable information on the role of the structure of the anionic component in determining the partitioning behavior of organic solutes. This knowledge is important for the design and optimization of ILs for specific applications, particularly as solvents in various industrial processes. The research also provides useful information about molecular interactions taking place in the interfaces of IL and organic additives in complex liquid media such as multicomponent electrolyte solutions, for example in energy storage applications.
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- 2024
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44. Nanomaterial Texture-Based Machine Learning of Ciprofloxacin Adsorption on Nanoporous Carbon.
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Käärik M, Krjukova N, Maran U, Oja M, Piir G, and Leis J
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- Adsorption, Porosity, Water Pollutants, Chemical chemistry, Water Purification methods, Anti-Bacterial Agents chemistry, Ciprofloxacin chemistry, Carbon chemistry, Nanostructures chemistry, Machine Learning, Nanopores
- Abstract
Drug substances in water bodies and groundwater have become a significant threat to the surrounding environment. This study focuses on the ability of the nanoporous carbon materials to remove ciprofloxacin from aqueous solutions under specific experimental conditions and on the development of the mathematical model that would allow describing the molecular interactions of the adsorption process and calculating the adsorption capacity of the material. Thus, based on the adsorption measurements of the 87 carbon materials, it was found that, depending on the porosity and pore size distribution, adsorption capacity values varied between 55 and 495 mg g
-1 . For a more detailed analysis of the effects of different carbon textures and pores characteristics, a Quantitative nano-Structure-Property Relationship (QnSPR) was developed to describe and predict the ability of a nanoporous carbon material to remove ciprofloxacin from aqueous solutions. The adsorption capacity of potential nanoporous carbon-based adsorbents for the removal of ciprofloxacin was shown to be sufficiently accurately described by a three-parameter multi-linear QnSPR equation (R2 = 0.70). This description was achieved only with parameters describing the texture of the carbon material such as specific surface area ( Sdft ) and pore size fractions of 1.1-1.2 nm (VN21.1-1.2) and 3.3-3.4 nm (VN23.3-3.4) for pores.- Published
- 2024
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45. Data-Driven Modelling of Substituted Pyrimidine and Uracil-Based Derivatives Validated with Newly Synthesized and Antiproliferative Evaluated Compounds.
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Zukić S, Osmanović A, Harej Hrkać A, Kraljević Pavelić S, Špirtović-Halilović S, Veljović E, Roca S, Trifunović S, Završnik D, and Maran U
- Subjects
- Humans, Machine Learning, Cell Line, Tumor, Uracil chemistry, Uracil analogs & derivatives, Uracil pharmacology, Uracil chemical synthesis, Pyrimidines chemistry, Pyrimidines pharmacology, Pyrimidines chemical synthesis, Quantitative Structure-Activity Relationship, Antineoplastic Agents pharmacology, Antineoplastic Agents chemistry, Antineoplastic Agents chemical synthesis, Cell Proliferation drug effects
- Abstract
The pyrimidine heterocycle plays an important role in anticancer research. In particular, the pyrimidine derivative families of uracil show promise as structural scaffolds relevant to cervical cancer. This group of chemicals lacks data-driven machine learning quantitative structure-activity relationships (QSARs) that allow for generalization and predictive capabilities in the search for new active compounds. To achieve this, a dataset of pyrimidine and uracil compounds from ChEMBL were collected and curated. A workflow was developed for data-driven machine learning QSAR using an intuitive dataset design and forwards selection of molecular descriptors. The model was thoroughly externally validated against available data. Blind validation was also performed by synthesis and antiproliferative evaluation of new synthesized uracil-based and pyrimidine derivatives. The most active compound among new synthesized derivatives, 2,4,5-trisubstituted pyrimidine was predicted with the QSAR model with differences of 0.02 compared to experimentally tested activity.
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- 2024
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46. Pesticide effect on earthworm lethality via interpretable machine learning.
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Kotli M, Piir G, and Maran U
- Subjects
- Animals, Bayes Theorem, Agriculture, Soil chemistry, Pesticides toxicity, Oligochaeta, Soil Pollutants analysis
- Abstract
Earthworms are among the most important animals (invertebrates) for soil health. Many chemical substances released into nature for agricultural development, such as pesticides, may have unwanted effects on those organisms. However, it is essential to understand the extent of the impact of chemicals on soil health first and then make the proper decisions for regulatory or commercial purposes. We hypothesize that there is an expressible quantitative structure-activity relationship (QSAR) between the structure of pesticide compounds and the acute toxicity effect of earthworm species Eisenia fetida. The description of this relationship allows for a better assessment of the impact of chemicals on the said earthworm. To describe this relationship, a dataset of chemicals was collected from open-access sources to develop a mathematical model. A novel approach, combining genetic algorithm and Bayesian optimization, was used to select structural features into the model and to optimize model parameters. The final QSAR classification model was created with the Random Forest algorithm and exhibited good prediction Accuracy of 0.78 on training set and 0.80 on test set. The model representation follows FAIR principles and is available on QsarDB.org., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)
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- 2024
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47. Interpretable machine learning for the identification of estrogen receptor agonists, antagonists, and binders.
- Author
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Piir G, Sild S, and Maran U
- Subjects
- Animals, Reproducibility of Results, Machine Learning, Protein Binding, Estrogens, Endocrine Disruptors toxicity, Endocrine Disruptors chemistry
- Abstract
An abnormal hormonal activity or exposure to endocrine-disrupting chemicals (EDCs) can cause endocrine system malfunction. Among the many interactions EDCs can affect is the disruption of estrogen signalling, which can lead to adverse health effects such as cancer, osteoporosis, neurodegenerative diseases, cardiovascular disease, insulin resistance, and obesity. Knowing which chemical can act as an EDC is a significant advantage and a practical necessity. New Approach Methodologies (NAM) computational models offer a quick and cost-effective solution for preliminary hazard assessment of chemicals without animal testing. Therefore, a machine learning approach was used to investigate the relationships between estrogen receptor (ER) activity and chemical structure to identify chemicals that can interact with ER. For this purpose, the consolidated in vitro assay data from ToxCast/Tox21 projects was used for developing Random Forest classification models for ER binding, agonists, and antagonists. The overall classification prediction accuracy reaches up to 82%, depending on whether the model predicted agonists, antagonists, or compounds that bind to the active site. Given the imbalance in endocrine disruption data, the derived models are good candidates for deprioritising chemicals and reducing animal testing. The interpretation of theoretical molecular descriptors of the models was consistent with the molecular interactions known in the ligand binding pocket. The estimated class probabilities enabled the analysis of the applicability domain of the developed models and the assessment of the predictions' reliability, followed by the guidelines for interpreting prediction results. The models are openly accessible and useable at QsarDB.org (http://dx.doi.org/10.15152/QDB.259) according to the FAIR (Findable, Accessible, Interoperable, Reusable) principles., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)
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- 2024
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48. ELIXIR and Toxicology: a community in development.
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Martens M, Stierum R, Schymanski EL, Evelo CT, Aalizadeh R, Aladjov H, Arturi K, Audouze K, Babica P, Berka K, Bessems J, Blaha L, Bolton EE, Cases M, Damalas DΕ, Dave K, Dilger M, Exner T, Geerke DP, Grafström R, Gray A, Hancock JM, Hollert H, Jeliazkova N, Jennen D, Jourdan F, Kahlem P, Klanova J, Kleinjans J, Kondic T, Kone B, Lynch I, Maran U, Martinez Cuesta S, Ménager H, Neumann S, Nymark P, Oberacher H, Ramirez N, Remy S, Rocca-Serra P, Salek RM, Sallach B, Sansone SA, Sanz F, Sarimveis H, Sarntivijai S, Schulze T, Slobodnik J, Spjuth O, Tedds J, Thomaidis N, Weber RJM, van Westen GJP, Wheelock CE, Williams AJ, Witters H, Zdrazil B, Županič A, and Willighagen EL
- Subjects
- Europe, Risk Assessment, Biological Science Disciplines
- Abstract
Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities., Competing Interests: No competing interests were disclosed., (Copyright: © 2023 Martens M et al.)
- Published
- 2023
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49. Intrinsic Aqueous Solubility: Mechanistically Transparent Data-Driven Modeling of Drug Substances.
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Oja M, Sild S, Piir G, and Maran U
- Abstract
Intrinsic aqueous solubility is a foundational property for understanding the chemical, technological, pharmaceutical, and environmental behavior of drug substances. Despite years of solubility research, molecular structure-based prediction of the intrinsic aqueous solubility of drug substances is still under active investigation. This paper describes the authors' systematic data-driven modelling in which two fit-for-purpose training data sets for intrinsic aqueous solubility were collected and curated, and three quantitative structure-property relationships were derived to make predictions for the most recent solubility challenge. All three models perform well individually, while being mechanistically transparent and easy to understand. Molecular descriptors involved in the models are related to the following key steps in the solubility process: dissociation of the molecule from the crystal, formation of a cavity in the solvent, and insertion of the molecule into the solvent. A consensus modeling approach with these models remarkably improved prediction capability and reduced the number of strong outliers by more than two times. The performance and outliers of the second solubility challenge predictions were analyzed retrospectively. All developed models have been published in the QsarDB.org repository according to FAIR principles and can be used without restrictions for exploring, downloading, and making predictions.
- Published
- 2022
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50. Machine Learning Quantitative Structure-Property Relationships as a Function of Ionic Liquid Cations for the Gas-Ionic Liquid Partition Coefficient of Hydrocarbons.
- Author
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Toots KM, Sild S, Leis J, Acree WE Jr, and Maran U
- Subjects
- Benzene, Cations, Cyclohexanes, Hexanes, Humans, Hydrocarbons, Machine Learning, Ionic Liquids chemistry
- Abstract
Ionic liquids (ILs) are known for their unique characteristics as solvents and electrolytes. Therefore, new ILs are being developed and adapted as innovative chemical environments for different applications in which their properties need to be understood on a molecular level. Computational data-driven methods provide means for understanding of properties at molecular level, and quantitative structure-property relationships (QSPRs) provide the framework for this. This framework is commonly used to study the properties of molecules in ILs as an environment. The opposite situation where the property is considered as a function of the ionic liquid does not exist. The aim of the present study was to supplement this perspective with new knowledge and to develop QSPRs that would allow the understanding of molecular interactions in ionic liquids based on the structure of the cationic moiety. A wide range of applications in electrochemistry, separation and extraction chemistry depends on the partitioning of solutes between the ionic liquid and the surrounding environment that is characterized by the gas-ionic liquid partition coefficient. To model this property as a function of the structure of a cationic counterpart, a series of ionic liquids was selected with a common bis-(trifluoromethylsulfonyl)-imide anion, [Tf2N]
- , for benzene, hexane and cyclohexane. MLR, SVR and GPR machine learning approaches were used to derive data-driven models and their performance was compared. The cross-validation coefficients of determination in the range 0.71-0.93 along with other performance statistics indicated a strong accuracy of models for all data series and machine learning methods. The analysis and interpretation of descriptors revealed that generally higher lipophilicity and dispersion interaction capability, and lower polarity in the cations induces a higher partition coefficient for benzene, hexane, cyclohexane and hydrocarbons in general. The applicability domain analysis of models concluded that there were no highly influential outliers and the models are applicable to a wide selection of cation families with variable size, polarity and aliphatic or aromatic nature.- Published
- 2022
- Full Text
- View/download PDF
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