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Tetko IV. Tox24 Challenge. Chem Res Toxicol 2024; 37:825-826. [PMID: 38769907 DOI: 10.1021/acs.chemrestox.4c00192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Affiliation(s)
- Igor V Tetko
- Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Munich-Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), DE-85764 Neuherberg, Germany
- BIGCHEM GmbH, Valerystr. 49, DE-85716 Unterschleißheim, Germany
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2
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Nik Mut NN, Na J, Jung J. A review on fate and ecotoxicity of biodegradable microplastics in aquatic system: Are biodegradable plastics truly safe for the environment? ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 344:123399. [PMID: 38242301 DOI: 10.1016/j.envpol.2024.123399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 01/14/2024] [Accepted: 01/17/2024] [Indexed: 01/21/2024]
Abstract
Plastic products are extensively used worldwide, but inadequate management of plastic waste results in significant plastic pollution. Biodegradable plastic (BPs) offers an alternative to traditional plastics, however, not all BPs can fully degrade under natural conditions. Instead, they may deteriorate into biodegradable microplastic (BMPs) at a faster rate than conventional plastic, thereby posing an additional hazard to aquatic environments. This study provides a comprehensive overview of the fate of BPs in aquatic systems and their eco-toxicological effects on aquatic organisms such as algae, invertebrates, and fish. The findings highlight that BMPs have comparable or heightened effects compared to conventional microplastics (MPs) which physiochemical characteristic of the polymer itself or by the chemical leached from the polymeric matrix can affect aquatic organisms. While BPs is not a flawless solution to address plastic pollution, future research should prioritize investigating their production, environmental behavior, ecological impact, and whether BMPs inflict greater harm than conventional MPs.
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Affiliation(s)
- Nik Nurhidayu Nik Mut
- Division of Environmental Science and Ecological Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Joorim Na
- OJEong Resilience Institute, Korea University, Seoul, 02841, Republic of Korea.
| | - Jinho Jung
- Division of Environmental Science and Ecological Engineering, Korea University, Seoul, 02841, Republic of Korea
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3
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Adardour M, Lasri M, Ait Lahcen M, Maatallah M, Idouhli R, Alanazi MM, Lahmidi S, Abouelfida A, Mague JT, Baouid A. Exploring the Efficacy of Benzimidazolone Derivative as Corrosion Inhibitors for Copper in a 3.5 wt.% NaCl Solution: A Comprehensive Experimental and Theoretical Investigation. Molecules 2023; 28:6948. [PMID: 37836791 PMCID: PMC10574370 DOI: 10.3390/molecules28196948] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 09/28/2023] [Accepted: 10/02/2023] [Indexed: 10/15/2023] Open
Abstract
This study focuses on the synthesis, theoretical analysis, and application of the corrosion inhibitor known as benzimidazolone, specifically 1-(cyclohex-1-enyl)-1,3-dihydro-2H-benzimiazol-2-one (CHBI). The structure of CHBI was determined by X-ray diffraction (XRD). The inhibitory properties of CHBI were investigated in a 3.5 wt.% NaCl solution on pure copper using various electrochemical techniques such as potentiodynamic polarization curves (PDPs) and electrochemical impedance spectroscopy (EIS), as well as scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX), UV-visible spectroscopy, and theoretical calculations. The obtained results indicate that CHBI is an excellent inhibitor, exhibiting remarkable effectiveness with an inhibition rate of 86.49% at 10-3 M. To further confirm the extent of adsorption of the inhibitory molecule on the copper surface, density functional theory (DFT) and Monte Carlo (MC) simulation studies were conducted. The results of this study demonstrate the synthesis and characterization of CHBI as a corrosion inhibitor. The experimental and theoretical analyses provide valuable insights into the inhibitory performance of CHBI, indicating its strong adsorption on the copper surface.
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Affiliation(s)
- Mohamed Adardour
- Laboratory of Chemistry Molecular, Department of Chemistry, Faculty of Sciences Semlalia, Cadi Ayyad University, B.P. 2390, Marrakech 40001, Morocco; (M.A.L.); (M.M.); (A.B.)
| | - Mohammed Lasri
- Applied Chemistry and Biomass Laboratory, Department of Chemistry, Faculty of Sciences Semlalia, Cadi Ayyad University, B.P. 2390, Marrakech 40001, Morocco; (M.L.); (R.I.); (A.A.)
| | - Marouane Ait Lahcen
- Laboratory of Chemistry Molecular, Department of Chemistry, Faculty of Sciences Semlalia, Cadi Ayyad University, B.P. 2390, Marrakech 40001, Morocco; (M.A.L.); (M.M.); (A.B.)
| | - Mohamed Maatallah
- Laboratory of Chemistry Molecular, Department of Chemistry, Faculty of Sciences Semlalia, Cadi Ayyad University, B.P. 2390, Marrakech 40001, Morocco; (M.A.L.); (M.M.); (A.B.)
| | - Rachid Idouhli
- Applied Chemistry and Biomass Laboratory, Department of Chemistry, Faculty of Sciences Semlalia, Cadi Ayyad University, B.P. 2390, Marrakech 40001, Morocco; (M.L.); (R.I.); (A.A.)
| | - Mohamed M. Alanazi
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia;
| | - Sanae Lahmidi
- Laboratory of Heterocyclic Organic Chemistry, Department of Chemistry, Faculty of Sciences, Mohammed V University in Rabat, Rabat 10106, Morocco;
| | - Abdesselam Abouelfida
- Applied Chemistry and Biomass Laboratory, Department of Chemistry, Faculty of Sciences Semlalia, Cadi Ayyad University, B.P. 2390, Marrakech 40001, Morocco; (M.L.); (R.I.); (A.A.)
| | - Joel T. Mague
- Department of Chemistry, Tulane University, New Orleans, LA 70118, USA;
| | - Abdesselam Baouid
- Laboratory of Chemistry Molecular, Department of Chemistry, Faculty of Sciences Semlalia, Cadi Ayyad University, B.P. 2390, Marrakech 40001, Morocco; (M.A.L.); (M.M.); (A.B.)
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Ksenofontov AA, Lukanov MM, Bocharov PS. Can machine learning methods accurately predict the molar absorption coefficient of different classes of dyes? SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 279:121442. [PMID: 35660154 DOI: 10.1016/j.saa.2022.121442] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
In this article, we provide a convenient tool for all researchers to predict the value of the molar absorption coefficient for a wide number of dyes without any computer costs. The new model is based on RFR method (ALogPS, OEstate + Fragmentor + QNPR) and is able to predict the molar absorption coefficient with an accuracy (5-fold cross-validation RMSE) of 0.26 log unit. This accuracy was achieved due to the fact that the model was trained on data for more than 20,000 unique dye molecules. To our knowledge, this is the first model for predicting the molar absorption coefficient trained on such a large and diverse set of dyes. The model is available at https://ochem.eu/article/145413. We hope that the new model will allow researchers to predict dyes with practically significant spectral characteristics and verify existing experimental data.
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Affiliation(s)
- Alexander A Ksenofontov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia.
| | - Michail M Lukanov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia; Ivanovo State University of Chemistry and Technology, 7, Sheremetevskiy Avenue, Ivanovo 153000, Russia
| | - Pavel S Bocharov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia
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Huang K, Zhang H. Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:12755-12764. [PMID: 35973069 DOI: 10.1021/acs.est.2c01764] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Machine learning (ML) is viewed as a promising tool for the prediction of aerobic biodegradation, one of the most important elimination pathways of organic chemicals from the environment. However, available models only have small datasets (<3200 records), make binary classification predictions, evaluate ready biodegradability, and do not incorporate experimental conditions (e.g., system setup and reaction time). This study addressed all these limitations by first compiling a large database of 12,750 records, considering both ready and inherent biodegradation under different conditions, and then developing regression and classification models using different chemical representations and ML algorithms. The best regression model (R2 = 0.54 and root mean square error of 0.25) and classification model (the prediction accuracy from 85.1%) achieved very good performance. The model interpretation indicated that the models correctly captured the effects of chemical substructures, following the order of C═O > O═C-O > OH > CH3 > halogen > branching > N > 6-member ring. The consideration of chemical speciation based on pKa and α notations did not affect the regression model performance but significantly improved the classification model performance (the accuracy increased to 87.6%). The models also showed large applicability domains and provided reasonable predictions for more than 98% of over 850,000 environmentally relevant chemicals in the Distributed Structure-Searchable Toxicity database. These robust, trustable models were finally made widely accessible through two free online predictors with graphical user interface.
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Affiliation(s)
- Kuan Huang
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - Huichun Zhang
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
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Ksenofontov AA, Lukanov MM, Bocharov PS, Berezin MB, Tetko IV. Deep neural network model for highly accurate prediction of BODIPYs absorption. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 267:120577. [PMID: 34776377 DOI: 10.1016/j.saa.2021.120577] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 10/12/2021] [Accepted: 10/31/2021] [Indexed: 06/13/2023]
Abstract
A possibility to accurately predict the absorption maximum wavelength of BODIPYs was investigated. We found that previously reported models had a low accuracy (40-57 nm) to predict BODIPYs due to the limited dataset sizes and/or number of BODIPYs (few hundreds). New models developed in this study were based on data of 6000-plus fluorescent dyes (including 4000-plus BODIPYs) and the deep neural network architecture. The high prediction accuracy (five-fold cross-validation room mean squared error (RMSE) of 18.4 nm) was obtained using a consensus model, which was more accurate than individual models. This model provided the excellent accuracy (RMSE of 8 nm) for molecules previously synthesized in our laboratory as well as for prospective validation of three new BODIPYs. We found that solvent properties did not significantly influence the model accuracy since only few BODIPYs exhibited solvatochromism. The analysis of large prediction errors suggested that compounds able to have intermolecular interactions with solvent or salts were likely to be incorrectly predicted. The consensus model is freely available at https://ochem.eu/article/134921 and can help the other researchers to accelerate design of new dyes with desired properties.
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Affiliation(s)
- Alexander A Ksenofontov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia.
| | - Michail M Lukanov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia; Ivanovo State University of Chemistry and Technology, 7, Sheremetevskiy Avenue, Ivanovo 153000, Russia
| | - Pavel S Bocharov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia; Ivanovo State University of Chemistry and Technology, 7, Sheremetevskiy Avenue, Ivanovo 153000, Russia
| | - Michail B Berezin
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia
| | - Igor V Tetko
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, 153045 Ivanovo, Russia; Helmholtz Zentrum München‑German Research Center for Environmental Health (GmbH), Institute of Structural Biology, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany; BIGCHEM GmbH, Valerystr. 49, 85716 Unterschleißheim, Germany
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8
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What Features of Ligands Are Relevant to the Opening of Cryptic Pockets in Drug Targets? INFORMATICS 2022. [DOI: 10.3390/informatics9010008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Small-molecule drug design aims to identify inhibitors that can specifically bind to a functionally important region on the target, i.e., an active site of an enzyme. Identification of potential binding pockets is typically based on static three-dimensional structures. However, small molecules may induce and select a dynamic binding pocket that is not visible in the apo protein, which presents a well-recognized challenge for structure-based drug discovery. Here, we assessed whether it is possible to identify features in molecules, which we refer to as inducers, that can induce the opening of cryptic pockets. The volume change between apo and bound protein conformations was used as a metric to differentiate chemical features in inducers vs. non-inducers. Based on the dataset of holo–apo pairs, classification models were built to determine an optimum threshold. The model analysis suggested that inducers preferred to be more hydrophobic and aromatic. The impact of sulfur was ambiguous, while phosphorus and halogen atoms were overrepresented in inducers. The fragment analysis showed that small changes in the structures of molecules can strongly affect the potential to induce a cryptic pocket. This analysis and developed model can be used to design inducers that can potentially open cryptic pockets for undruggable proteins.
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Rusanov AI, Dmitrieva OA, Mamardashvili NZ, Tetko IV. More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins. Int J Mol Sci 2022. [DOI: https://doi.org/10.3390/ijms23031201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.
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Rusanov AI, Dmitrieva OA, Mamardashvili NZ, Tetko IV. More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins. Int J Mol Sci 2022; 23:ijms23031201. [PMID: 35163123 PMCID: PMC8835262 DOI: 10.3390/ijms23031201] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 01/19/2022] [Indexed: 02/05/2023] Open
Abstract
The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.
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Affiliation(s)
- Aleksey I. Rusanov
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, 153045 Ivanovo, Russia; (A.I.R.); (O.A.D.); (N.Z.M.)
| | - Olga A. Dmitrieva
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, 153045 Ivanovo, Russia; (A.I.R.); (O.A.D.); (N.Z.M.)
| | - Nugzar Zh. Mamardashvili
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, 153045 Ivanovo, Russia; (A.I.R.); (O.A.D.); (N.Z.M.)
| | - Igor V. Tetko
- G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, 153045 Ivanovo, Russia; (A.I.R.); (O.A.D.); (N.Z.M.)
- Helmholtz Munich, Institute of Structural Biology, Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), D-85764 Neuherberg, Germany
- BIGCHEM GmbH, D-85716 Unterschleißheim, Germany
- Correspondence: ; Tel.: +49-89-3187-3575
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Rusanov AI, Dmitrieva OA, Mamardashvili NZ, Tetko IV. More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins. Int J Mol Sci 2022. [DOI: https:/doi.org/10.3390/ijms23031201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.
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Makarov D, Fadeeva Y, Shmukler L, Tetko I. Beware of proper validation of models for ionic Liquids! J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.117722] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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13
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Sharma SR, Singh B, Kaur M. Hybrid SFO and TLBO optimization for biodegradable classification. Soft comput 2021. [DOI: 10.1007/s00500-021-06196-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Li Y, Xu Y, Yu Y. CRNNTL: Convolutional Recurrent Neural Network and Transfer Learning for QSAR Modeling in Organic Drug and Material Discovery. Molecules 2021; 26:molecules26237257. [PMID: 34885843 PMCID: PMC8658888 DOI: 10.3390/molecules26237257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 11/25/2021] [Accepted: 11/26/2021] [Indexed: 11/16/2022] Open
Abstract
Molecular latent representations, derived from autoencoders (AEs), have been widely used for drug or material discovery over the past couple of years. In particular, a variety of machine learning methods based on latent representations have shown excellent performance on quantitative structure–activity relationship (QSAR) modeling. However, the sequence feature of them has not been considered in most cases. In addition, data scarcity is still the main obstacle for deep learning strategies, especially for bioactivity datasets. In this study, we propose the convolutional recurrent neural network and transfer learning (CRNNTL) method inspired by the applications of polyphonic sound detection and electrocardiogram classification. Our model takes advantage of both convolutional and recurrent neural networks for feature extraction, as well as the data augmentation method. According to QSAR modeling on 27 datasets, CRNNTL can outperform or compete with state-of-art methods in both drug and material properties. In addition, the performances on one isomers-based dataset indicate that its excellent performance results from the improved ability in global feature extraction when the ability of the local one is maintained. Then, the transfer learning results show that CRNNTL can overcome data scarcity when choosing relative source datasets. Finally, the high versatility of our model is shown by using different latent representations as inputs from other types of AEs.
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Affiliation(s)
- Yaqin Li
- West China Tianfu Hospital, Sichuan University, Chengdu 610041, China
- Correspondence: (Y.L.); (Y.Y.)
| | - Yongjin Xu
- Department of Chemistry and Molecular Biology, University of Gothenburg, Kemivägen 10, 41296 Gothenburg, Sweden;
| | - Yi Yu
- Department of Chemistry and Molecular Biology, University of Gothenburg, Kemivägen 10, 41296 Gothenburg, Sweden;
- Correspondence: (Y.L.); (Y.Y.)
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Exploring the octanol-water partition coefficient dataset using deep learning techniques and data augmentation. Commun Chem 2021; 4:90. [PMID: 36697535 PMCID: PMC9814212 DOI: 10.1038/s42004-021-00528-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 05/21/2021] [Indexed: 01/28/2023] Open
Abstract
Today more and more data are freely available. Based on these big datasets deep neural networks (DNNs) rapidly gain relevance in computational chemistry. Here, we explore the potential of DNNs to predict chemical properties from chemical structures. We have selected the octanol-water partition coefficient (log P) as an example, which plays an essential role in environmental chemistry and toxicology but also in chemical analysis. The predictive performance of the developed DNN is good with an rmse of 0.47 log units in the test dataset and an rmse of 0.33 for an external dataset from the SAMPL6 challenge. To this end, we trained the DNN using data augmentation considering all potential tautomeric forms of the chemicals. We further demonstrate how DNN models can help in the curation of the log P dataset by identifying potential errors, and address limitations of the dataset itself.
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16
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Karpov P, Godin G, Tetko IV. Transformer-CNN: Swiss knife for QSAR modeling and interpretation. J Cheminform 2020; 12:17. [PMID: 33431004 PMCID: PMC7079452 DOI: 10.1186/s13321-020-00423-w] [Citation(s) in RCA: 107] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Accepted: 03/09/2020] [Indexed: 01/03/2023] Open
Abstract
We present SMILES-embeddings derived from the internal encoder state of a Transformer [1] model trained to canonize SMILES as a Seq2Seq problem. Using a CharNN [2] architecture upon the embeddings results in higher quality interpretable QSAR/QSPR models on diverse benchmark datasets including regression and classification tasks. The proposed Transformer-CNN method uses SMILES augmentation for training and inference, and thus the prognosis is based on an internal consensus. That both the augmentation and transfer learning are based on embeddings allows the method to provide good results for small datasets. We discuss the reasons for such effectiveness and draft future directions for the development of the method. The source code and the embeddings needed to train a QSAR model are available on https://github.com/bigchem/transformer-cnn. The repository also has a standalone program for QSAR prognosis which calculates individual atoms contributions, thus interpreting the model’s result. OCHEM [3] environment (https://ochem.eu) hosts the on-line implementation of the method proposed.
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Affiliation(s)
- Pavel Karpov
- Institute of Structural Biology, Helmholtz Zentrum München-Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany. .,BIGCHEM GmbH, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
| | - Guillaume Godin
- Firmenich International SA, Digital Lab, Geneva, Lausanne, Switzerland
| | - Igor V Tetko
- Institute of Structural Biology, Helmholtz Zentrum München-Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.,BIGCHEM GmbH, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
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Cui X, Yang R, Li S, Liu J, Wu Q, Li X. Modeling and insights into molecular basis of low molecular weight respiratory sensitizers. Mol Divers 2020; 25:847-859. [PMID: 32166484 DOI: 10.1007/s11030-020-10069-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Accepted: 03/03/2020] [Indexed: 01/10/2023]
Abstract
Respiratory sensitization has been considered an important toxicological endpoint, because of the severe risk to human health. A great part of sensitization events were caused by low molecular weight (< 1000) respiratory sensitizers in the past decades. However, there is currently no widely accepted test method that can identify prospective low molecular weight respiratory sensitisers. Herein, we performed the study of modeling and insights into molecular basis of low molecular weight respiratory sensitizers with a high-quality data set containing 136 respiratory sensitizers and 518 nonsensitizers. We built a number of classification models by using OCHEM tools, and a consensus model was developed based on the ten best individual models. The consensus model showed good predictive ability with a balanced accuracy of 0.78 and 0.85 on fivefold cross-validation and external validation, respectively. The readers can predict the respiratory sensitization of organic compounds via https://ochem.eu/article/114857 . The effect of several molecular properties on respiratory sensitization was also evaluated. The results indicated that these properties differ significantly between respiratory sensitizers and nonsensitizers. Furthermore, 14 privileged substructures responsible for respiratory sensitization were identified. We hope the models and the findings could provide useful help for environmental risk assessment.
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Affiliation(s)
- Xueyan Cui
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China
| | - Rui Yang
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China
| | - Siwen Li
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China
| | - Juan Liu
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China
| | - Qiuyun Wu
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China
| | - Xiao Li
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China. .,Department of Clinical pharmacy, The First Affiliated Hospital of Shandong First Medical University, Shandong First Medical University, Jinan, 250014, China.
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18
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Tarasova OA, Biziukova NY, Filimonov DA, Poroikov VV, Nicklaus MC. Data Mining Approach for Extraction of Useful Information About Biologically Active Compounds from Publications. J Chem Inf Model 2019; 59:3635-3644. [PMID: 31453694 DOI: 10.1021/acs.jcim.9b00164] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
A lot of high quality data on the biological activity of chemical compounds are required throughout the whole drug discovery process: from development of computational models of the structure-activity relationship to experimental testing of lead compounds and their validation in clinics. Currently, a large amount of such data is available from databases, scientific publications, and patents. Biological data are characterized by incompleteness, uncertainty, and low reproducibility. Despite the existence of free and commercially available databases of biological activities of compounds, they usually lack unambiguous information about peculiarities of biological assays. On the other hand, scientific papers are the primary source of new data disclosed to the scientific community for the first time. In this study, we have developed and validated a data-mining approach for extraction of text fragments containing description of bioassays. We have used this approach to evaluate compounds and their biological activity reported in scientific publications. We have found that categorization of papers into relevant and irrelevant may be performed based on the machine-learning analysis of the abstracts. Text fragments extracted from the full texts of publications allow their further partitioning into several classes according to the peculiarities of bioassays. We demonstrate the applicability of our approach to the comparison of the endpoint values of biological activity and cytotoxicity of reference compounds.
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Affiliation(s)
- Olga A Tarasova
- Department of Bioinformatics , Institute of Biomedical Chemistry , 10 Building 8, Pogodinskaya Street , Moscow 119121 , Russia
| | - Nadezhda Yu Biziukova
- Department of Bioinformatics , Institute of Biomedical Chemistry , 10 Building 8, Pogodinskaya Street , Moscow 119121 , Russia
| | - Dmitry A Filimonov
- Department of Bioinformatics , Institute of Biomedical Chemistry , 10 Building 8, Pogodinskaya Street , Moscow 119121 , Russia
| | - Vladimir V Poroikov
- Department of Bioinformatics , Institute of Biomedical Chemistry , 10 Building 8, Pogodinskaya Street , Moscow 119121 , Russia
| | - Marc C Nicklaus
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research , National Cancer Institute , Frederick , Maryland 21702 , United States
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19
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Cui X, Liu J, Zhang J, Wu Q, Li X. In silico prediction of drug‐induced rhabdomyolysis with machine‐learning models and structural alerts. J Appl Toxicol 2019; 39:1224-1232. [DOI: 10.1002/jat.3808] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 03/13/2019] [Accepted: 03/17/2019] [Indexed: 12/21/2022]
Affiliation(s)
- Xueyan Cui
- Department of Pharmacy, Shandong Provincial Qianfoshan HospitalShandong University Jinan China
| | - Juan Liu
- Department of Pharmacy, Shandong Provincial Qianfoshan HospitalShandong University Jinan China
| | - Jinfeng Zhang
- Department of Pharmacy, Shandong Provincial Qianfoshan HospitalShandong University Jinan China
| | - Qiuyun Wu
- Department of Pharmacy, Shandong Provincial Qianfoshan HospitalShandong University Jinan China
| | - Xiao Li
- Department of Pharmacy, Shandong Provincial Qianfoshan HospitalShandong University Jinan China
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20
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Mokhnache K, Karbab A, Charef N, Arrar L, Mubarak MS. Synthesis, characterization, superoxide anion scavenging evaluation, skin sensitization predictions, and DFT calculations for a new isonicotinylhydrazide analog. J Mol Struct 2019. [DOI: 10.1016/j.molstruc.2018.11.052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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21
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Rücker C, Mahmoud WMM, Schwartz D, Kümmerer K. Biodegradation tests of mercaptocarboxylic acids, their esters, related divalent sulfur compounds and mercaptans. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2018; 25:18393-18411. [PMID: 29667058 PMCID: PMC6061509 DOI: 10.1007/s11356-018-1812-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Accepted: 03/19/2018] [Indexed: 06/08/2023]
Abstract
Mercaptocarboxylic acids and their esters, a class of difunctional compounds bearing both a mercapto and a carboxylic acid or ester functional group, are industrial chemicals of potential environmental concern. Biodegradation of such compounds was systematically investigated here, both by literature search and by experiments (Closed Bottle Test OECD 301D and Manometric Respirometry Test OECD 301F). These compounds were found either readily biodegradable or at least biodegradable to a significant extent. Some related compounds of divalent sulfur were tested for comparison (mercaptans, sulfides, disulfides). For the two relevant monofunctional compound classes, carboxylic acids/esters and mercaptans, literature data were compiled, and by comparison with structurally similar compounds without these functional groups, the influence of COOH/COOR' and SH groups on biodegradability was evaluated. Thereby, an existing rule of thumb for biodegradation of carboxylic acids/esters was supported by experimental data, and a rule of thumb could be formulated for mercaptans. Concurrent to biodegradation, abiotic processes were observed in the experiments, rapid oxidative formation of disulfides (dimerisation of monomercaptans and cyclisation of dimercaptans) and hydrolysis of esters. Some problems that compromise the reproducibility of biodegradation test results were discussed.
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Affiliation(s)
- Christoph Rücker
- Institute of Sustainable and Environmental Chemistry, Leuphana University Lüneburg, Universitätsallee 1, 21335, Lüneburg, Germany.
| | - Waleed M M Mahmoud
- Institute of Sustainable and Environmental Chemistry, Leuphana University Lüneburg, Universitätsallee 1, 21335, Lüneburg, Germany
- Analytical Chemistry Department, Faculty of Pharmacy, Suez Canal University, Ismailia, 41522, Egypt
| | - Dirk Schwartz
- Bruno Bock Thiochemicals, 21436, Marschacht, Germany
| | - Klaus Kümmerer
- Institute of Sustainable and Environmental Chemistry, Leuphana University Lüneburg, Universitätsallee 1, 21335, Lüneburg, Germany
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22
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Trush MM, Kovalishyn V, Ocheretniuk AD, Kobzar OL, Kachaeva MV, Brovarets VS, Metelytsia LO. QSAR Study of Some 1,3-Oxazolylphosphonium Derivatives as New Potent Anti-Candida Agents and Their Toxicity Evaluation. Curr Drug Discov Technol 2018; 16:204-209. [PMID: 29669499 DOI: 10.2174/1570163815666180418145422] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 04/04/2018] [Accepted: 04/05/2018] [Indexed: 11/22/2022]
Abstract
BACKGROUND The incidence of invasive fungal infections caused by Candida spp. has increased continuously in recent decades, especially in populations of immunocompromised patients or individuals hospitalized with serious underlying diseases. Therefore, the goal of our study was the search for new potent Candida albicans inhibitors via the development of QSAR models that could speed up this search process. A number of the most promising 1,3-oxazol-4-yltriphenylphosphonium derivatives with predicted activities were synthesized and experimentally tested. Furthermore, the toxicity of the studied compounds was determined in vitro using acetylcholinesterase enzyme as a biological marker. METHODS The classification QSAR models were created using Random Forests (WEKA-RF), k-Nearest Neighbors and Associative Neural Networks methods and different combinations of descriptors on the Online Chemical Modeling Environment (OCHEM) platform. Аntifungal properties of the investigated compounds were performed using standard disk diffusion method. The enzyme inhibitory action of the compounds was determined by modified Ellman's method using acetylcholinesterase from the electric organ of Electrophorus electricus. RESULTS Three classification QSAR models were developed by the WEKA-RF, k-NN and ASNN methods using the ALogPS, E-State indices and Dragon v.7 descriptors. The predictive ability of the models was tested through cross-validation, giving a balanced accuracy BA = 80-91%. All compounds demonstrated good antifungal properties against Candida spp. and slight inhibition of the acetylcholinesterase activity. CONCLUSION The high percentage of coincidence between the QSAR predictions and the experimental results confirmed the high predictive power of the developed QSAR models that can be applied as tools for finding new potential inhibitors against Candida spp. Furthermore, 1,3-oxazol-4- yl(triphenyl)phosphonium salts could be considered as promising candidates for the treatment of candidiasis and the disinfection of medical equipment.
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Affiliation(s)
- Maria M Trush
- Department of Medical and Biological Researches, Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Sciences of Ukraine, Kyiv 02094, Ukraine
| | - Vasyl Kovalishyn
- Department of Medical and Biological Researches, Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Sciences of Ukraine, Kyiv 02094, Ukraine
| | - Alla D Ocheretniuk
- Department of Bioorganic Mechanisms, Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Sciences of Ukraine, Kyiv 02094, Ukraine
| | - Oleksandr L Kobzar
- Department of Bioorganic Mechanisms, Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Sciences of Ukraine, Kyiv 02094, Ukraine
| | - Maryna V Kachaeva
- Department of Chemistry of Bioactive Nitrogen Containing Heterocyclic Bases, Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Sciences of Ukraine, Kyiv 02094, Ukraine
| | - Volodymyr S Brovarets
- Department of Chemistry of Bioactive Nitrogen Containing Heterocyclic Bases, Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Sciences of Ukraine, Kyiv 02094, Ukraine
| | - Larisa O Metelytsia
- Department of Medical and Biological Researches, Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Sciences of Ukraine, Kyiv 02094, Ukraine
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23
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Ghosh D, Koch U, Hadian K, Sattler M, Tetko IV. Luciferase Advisor: High-Accuracy Model To Flag False Positive Hits in Luciferase HTS Assays. J Chem Inf Model 2018; 58:933-942. [PMID: 29667823 DOI: 10.1021/acs.jcim.7b00574] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Firefly luciferase is an enzyme that has found ubiquitous use in biological assays in high-throughput screening (HTS) campaigns. The inhibition of luciferase in such assays could lead to a false positive result. This issue has been known for a long time, and there have been significant efforts to identify luciferase inhibitors in order to enhance recognition of false positives in screening assays. However, although a large amount of publicly accessible luciferase counterscreen data is available, to date little effort has been devoted to building a chemoinformatic model that can identify such molecules in a given data set. In this study we developed models to identify these molecules using various methods, such as molecular docking, SMARTS screening, pharmacophores, and machine learning methods. Among the structure-based methods, the pharmacophore-based method showed promising results, with a balanced accuracy of 74.2%. However, machine-learning approaches using associative neural networks outperformed all of the other methods explored, producing a final model with a balanced accuracy of 89.7%. The high predictive accuracy of this model is expected to be useful for advising which compounds are potential luciferase inhibitors present in luciferase HTS assays. The models developed in this work are freely available at the OCHEM platform at http://ochem.eu .
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Affiliation(s)
- Dipan Ghosh
- Institute of Structural Biology , Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH) , Ingolstaedter Landstrasse 1 , 85764 Neuherberg , Germany
| | - Uwe Koch
- Lead Discovery Center GmbH , Otto-Hahn-Straße 15 , 44227 Dortmund , Germany
| | - Kamyar Hadian
- Assay Development and Screening Platform , Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH) , Ingolstaedter Landstrasse 1 , 85764 Neuherberg , Germany
| | - Michael Sattler
- Bayerisches NMR-Zentrum, Department of Chemistry , Technical University of Munich , Ernst-Otto-Fischer-Straße 2 , 85747 Garching , Germany
| | - Igor V Tetko
- Institute of Structural Biology , Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH) , Ingolstaedter Landstrasse 1 , 85764 Neuherberg , Germany.,BIGCHEM GmbH , Ingolstaedter Landstrasse 1 b. 60w , 85764 Neuherberg , Germany
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24
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Withnall M, Chen H, Tetko IV. Matched Molecular Pair Analysis on Large Melting Point Datasets: A Big Data Perspective. ChemMedChem 2018; 13:599-606. [PMID: 28650584 PMCID: PMC5900986 DOI: 10.1002/cmdc.201700303] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Revised: 06/26/2017] [Indexed: 11/11/2022]
Abstract
A matched molecular pair (MMP) analysis was used to examine the change in melting point (MP) between pairs of similar molecules in a set of ∼275k compounds. We found many cases in which the change in MP (ΔMP) of compounds correlates with changes in functional groups. In line with the results of a previous study, correlations between ΔMP and simple molecular descriptors, such as the number of hydrogen bond donors, were identified. In using a larger dataset, covering a wider chemical space and range of melting points, we observed that this method remains stable and scales well with larger datasets. This MMP-based method could find use as a simple privacy-preserving technique to analyze large proprietary databases and share findings between participating research groups.
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Affiliation(s)
- Michael Withnall
- Helmholtz Zentrum München—German Research Center for Environmental Health, GmbHInstitute of Structural BiologyNeuherbergGermany
| | - Hongming Chen
- External Sciences, Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D GothenburgMölndal43183Sweden
| | - Igor V. Tetko
- Helmholtz Zentrum München—German Research Center for Environmental Health, GmbHInstitute of Structural BiologyNeuherbergGermany
- BIGCHEM GmbHIngolstädter Landstraße 1, b. 60w85764NeuherbergGermany
- Institute of Structural Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, GmbHIngolstädter Landstraße 185764NeuherbergGermany
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25
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Nolte TM, Pinto-Gil K, Hendriks AJ, Ragas AMJ, Pastor M. Quantitative structure-activity relationships for primary aerobic biodegradation of organic chemicals in pristine surface waters: starting points for predicting biodegradation under acclimatization. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2018; 20:157-170. [PMID: 29192704 DOI: 10.1039/c7em00375g] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Microbial biomass and acclimation can affect the removal of organic chemicals in natural surface waters. In order to account for these effects and develop more robust models for biodegradation, we have compiled and curated removal data for un-acclimated (pristine) surface waters on which we developed quantitative structure-activity relationships (QSARs). Global analysis of the very heterogeneous dataset including neutral, anionic, cationic and zwitterionic chemicals (N = 233) using a random forest algorithm showed that useful predictions were possible (Qext2 = 0.4-0.5) though relatively large standard errors were associated (SDEP ∼0.7). Classification of the chemicals based on speciation state and metabolic pathway showed that biodegradation is influenced by the two, and that the dependence of biodegradation on chemical characteristics is non-linear. Class-specific QSAR analysis indicated that shape and charge distribution determine the biodegradation of neutral chemicals (R2 ∼ 0.6), e.g. through membrane permeation or binding to P450 enzymes, whereas the average biodegradation of charged chemicals is 1 to 2 orders of magnitude lower, for which degradation depends more directly on cellular uptake (R2 ∼ 0.6). Further analysis showed that specific chemical classes such as peptides and organic halogens are relatively less biodegradable in pristine surface waters, resulting in the need for the microbial consortia to acclimate. Additional literature data was used to verify an acclimation model (based on Monod-type kinetics) capable of extrapolating QSAR predictions to acclimating conditions such as in water treatment, downstream lakes and large rivers under μg L-1 to mg L-1 concentrations. The framework developed, despite being based on multiple assumptions, is promising and needs further validation using experimentation with more standardised and homogenised conditions as well as adequate characterization of the inoculum used.
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Affiliation(s)
- Tom M Nolte
- Department of Environmental Science, Institute for Water and Wetland Research, Radboud University Nijmegen, P. O. Box 9010, 6500 GL Nijmegen, The Netherlands.
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26
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Li X, Zhang Y, Li H, Zhao Y. Modeling of the hERG K+ Channel Blockage Using Online Chemical Database and Modeling Environment (OCHEM). Mol Inform 2017; 36. [PMID: 28857516 DOI: 10.1002/minf.201700074] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 08/08/2017] [Indexed: 11/06/2022]
Abstract
Human ether-a-go-go related gene (hERG) K+ channel plays an important role in cardiac action potential. Blockage of hERG channel may result in long QT syndrome (LQTS), even cause sudden cardiac death. Many drugs have been withdrawn from the market because of the serious hERG-related cardiotoxicity. Therefore, it is quite essential to estimate the chemical blockage of hERG in the early stage of drug discovery. In this study, a diverse set of 3721 compounds with hERG inhibition data was assembled from literature. Then, we make full use of the Online Chemical Modeling Environment (OCHEM), which supplies rich machine learning methods and descriptor sets, to build a series of classification models for hERG blockage. We also generated two consensus models based on the top-performing individual models. The consensus models performed much better than the individual models both on 5-fold cross validation and external validation. Especially, consensus model II yielded the prediction accuracy of 89.5 % and MCC of 0.670 on external validation. This result indicated that the predictive power of consensus model II should be stronger than most of the previously reported models. The 17 top-performing individual models and the consensus models and the data sets used for model development are available at https://ochem.eu/article/103592.
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Affiliation(s)
- Xiao Li
- Beijing Computing Center, Beijing Academy of Science and Technology, 7 Fengxian road, Beijing, 100094, China.,Beijing Beike Deyuan Bio-Pharm Technology Co.Ltd, 7 Fengxian road, Beijing, 100094, China
| | - Yuan Zhang
- Beijing Beike Deyuan Bio-Pharm Technology Co.Ltd, 7 Fengxian road, Beijing, 100094, China
| | - Huanhuan Li
- Beijing Beike Deyuan Bio-Pharm Technology Co.Ltd, 7 Fengxian road, Beijing, 100094, China
| | - Yong Zhao
- Beijing Computing Center, Beijing Academy of Science and Technology, 7 Fengxian road, Beijing, 100094, China.,Beijing Beike Deyuan Bio-Pharm Technology Co.Ltd, 7 Fengxian road, Beijing, 100094, China
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27
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Latino DARS, Wicker J, Gütlein M, Schmid E, Kramer S, Fenner K. Eawag-Soil in enviPath: a new resource for exploring regulatory pesticide soil biodegradation pathways and half-life data. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2017; 19:449-464. [PMID: 28229138 DOI: 10.1039/c6em00697c] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Developing models for the prediction of microbial biotransformation pathways and half-lives of trace organic contaminants in different environments requires as training data easily accessible and sufficiently large collections of respective biotransformation data that are annotated with metadata on study conditions. Here, we present the Eawag-Soil package, a public database that has been developed to contain all freely accessible regulatory data on pesticide degradation in laboratory soil simulation studies for pesticides registered in the EU (282 degradation pathways, 1535 reactions, 1619 compounds and 4716 biotransformation half-life values with corresponding metadata on study conditions). We provide a thorough description of this novel data resource, and discuss important features of the pesticide soil degradation data that are relevant for model development. Most notably, the variability of half-life values for individual compounds is large and only about one order of magnitude lower than the entire range of median half-life values spanned by all compounds, demonstrating the need to consider study conditions in the development of more accurate models for biotransformation prediction. We further show how the data can be used to find missing rules relevant for predicting soil biotransformation pathways. From this analysis, eight examples of reaction types were presented that should trigger the formulation of new biotransformation rules, e.g., Ar-OH methylation, or the extension of existing rules, e.g., hydroxylation in aliphatic rings. The data were also used to exemplarily explore the dependence of half-lives of different amide pesticides on chemical class and experimental parameters. This analysis highlighted the value of considering initial transformation reactions for the development of meaningful quantitative-structure biotransformation relationships (QSBR), which is a novel opportunity offered by the simultaneous encoding of transformation reactions and corresponding half-lives in Eawag-Soil. Overall, Eawag-Soil provides an unprecedentedly rich collection of manually extracted and curated biotransformation data, which should be useful in a great variety of applications.
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Affiliation(s)
- Diogo A R S Latino
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland.
| | - Jörg Wicker
- Institute of Computer Science, Johannes Gutenberg University Mainz, 55128 Mainz, Germany
| | - Martin Gütlein
- Institute of Computer Science, Johannes Gutenberg University Mainz, 55128 Mainz, Germany
| | - Emanuel Schmid
- Scientific IT Services, ETH Zürich, 8092 Zürich, Switzerland
| | - Stefan Kramer
- Institute of Computer Science, Johannes Gutenberg University Mainz, 55128 Mainz, Germany
| | - Kathrin Fenner
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland. and Department of Chemistry, University of Zürich, 8057 Zürich, Switzerland
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28
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Tetko IV, Maran U, Tropsha A. Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development. Mol Inform 2016; 36. [PMID: 27778468 DOI: 10.1002/minf.201600082] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Accepted: 10/03/2016] [Indexed: 01/08/2023]
Abstract
Thousands 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.
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Affiliation(s)
- Igor V Tetko
- Institute of Structural Biology, Helmholtz Zentrum München -, German Research Center for Environmental Health (GmbH), Institute of Structural Biology, Ingolstädter Landstraße 1, D-, 85764, Neuherberg, Germany.,BigChem GmbH, Ingolstädter Landstraße 1, b. 60w, D-, 85764, Neuherberg, Germany
| | - Uko Maran
- Institute of Chemistry, University of Tartu, Ravila 14A, Tartu, 50411, Estonia
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.,Butlerov Institute of Chemistry, Kazan Federal University, Kremlyovskaya St. 18, 420008, Kazan, Russia
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29
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de Carvalho Rocha WF, Sheen DA. Classification of biodegradable materials using QSAR modelling with uncertainty estimation. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:799-811. [PMID: 27710037 PMCID: PMC5382130 DOI: 10.1080/1062936x.2016.1238010] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Accepted: 09/13/2016] [Indexed: 06/06/2023]
Abstract
The ability to determine the biodegradability of chemicals without resorting to expensive tests is ecologically and economically desirable. Models based on quantitative structure-activity relations (QSAR) provide some promise in this direction. However, QSAR models in the literature rarely provide uncertainty estimates in more detail than aggregated statistics such as the sensitivity and specificity of the model's predictions. Almost never is there a means of assessing the uncertainty in an individual prediction. Without an uncertainty estimate, it is impossible to assess the trustworthiness of any particular prediction, which leaves the model with a low utility for regulatory purposes. In the present work, a QSAR model with uncertainty estimates is used to predict biodegradability for a set of substances from a publicly available data set. Separation was performed using a partial least squares discriminant analysis model, and the uncertainty was estimated using bootstrapping. The uncertainty prediction allows for confidence intervals to be assigned to any of the model's predictions, allowing for a more complete assessment of the model than would be possible through a traditional statistical analysis. The results presented here are broadly applicable to other areas of modelling as well, because the calculation of the uncertainty will clearly demonstrate where additional tests are needed.
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Affiliation(s)
| | - David Allan Sheen
- Chemical Sciences Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
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30
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Hodyna D, Kovalishyn V, Rogalsky S, Blagodatnyi V, Petko K, Metelytsia L. Antibacterial Activity of Imidazolium-Based Ionic Liquids Investigated by QSAR Modeling and Experimental Studies. Chem Biol Drug Des 2016; 88:422-33. [DOI: 10.1111/cbdd.12770] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 03/11/2016] [Accepted: 03/28/2016] [Indexed: 01/28/2023]
Affiliation(s)
- Diana Hodyna
- Department of Medical and Biological Researches; Institute of Bioorganic Chemistry and Petrochemistry; NAS of Ukraine; 1, Murmanska Street Kyiv 02160 Ukraine
| | - Vasyl Kovalishyn
- Department of Medical and Biological Researches; Institute of Bioorganic Chemistry and Petrochemistry; NAS of Ukraine; 1, Murmanska Street Kyiv 02160 Ukraine
| | - Sergiy Rogalsky
- Laboratory of Modification of Polymers; Institute of Bioorganic Chemistry and Petrochemistry; NAS of Ukraine; 50, Kharkivske Schose Kyiv 02160 Ukraine
| | - Volodymyr Blagodatnyi
- Department of Microbiology and Epidemiology; Shupyk National Medical Academy of Postgraduate Education; 9, Dorogozhytska Street Kyiv 04112 Ukraine
| | - Kirill Petko
- Organofluorine Compounds Chemistry Department; Institute of Organic Chemistry; NAS of Ukraine; 5, Murmanska Street Kyiv 02660 Ukraine
| | - Larisa Metelytsia
- Department of Medical and Biological Researches; Institute of Bioorganic Chemistry and Petrochemistry; NAS of Ukraine; 1, Murmanska Street Kyiv 02160 Ukraine
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31
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Novotarskyi S, Abdelaziz A, Sushko Y, Körner R, Vogt J, Tetko IV. ToxCast EPA in Vitro to in Vivo Challenge: Insight into the Rank-I Model. Chem Res Toxicol 2016; 29:768-75. [PMID: 27120770 PMCID: PMC5413193 DOI: 10.1021/acs.chemrestox.5b00481] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
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The
ToxCast EPA challenge was managed by TopCoder in Spring 2014.
The goal of the challenge was to develop a model to predict the lowest
effect level (LEL) concentration based on in vitro measurements and calculated in silico descriptors.
This article summarizes the computational steps used to develop the
Rank-I model, which calculated the lowest prediction error for the
secret test data set of the challenge. The model was developed using
the publicly available Online CHEmical database and Modeling environment
(OCHEM), and it is freely available at http://ochem.eu/article/68104. Surprisingly, this model does not use any in vitro measurements. The logic of the decision steps used to develop the
model and the reason to skip inclusion of in vitro measurements is described. We also show that inclusion of in vitro assays would not improve the accuracy of the model.
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Affiliation(s)
| | - Ahmed Abdelaziz
- Rosettastein Consulting (UG) , D-85354 Freising, Germany.,Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, TUM-Technische Universität München , Freising, Germany
| | - Yurii Sushko
- eADMET GmbH , Lichtenbergstraße 8, D-85748 Garching, Munich, Germany
| | - Robert Körner
- eADMET GmbH , Lichtenbergstraße 8, D-85748 Garching, Munich, Germany
| | - Joachim Vogt
- eADMET GmbH , Lichtenbergstraße 8, D-85748 Garching, Munich, Germany
| | - Igor V Tetko
- Helmholtz Zentrum München - Research Center for Environmental Health (GmbH), Institute of Structural Biology , Ingolstädter Landstraße 1 b. 60w, D-85764 Neuherberg, Germany.,BigChem GmbH , Ingolstädter Landstraße 1 b. 60w, D-85764 Neuherberg, Germany
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Tetko IV, Varbanov HP, Galanski MS, Talmaciu M, Platts JA, Ravera M, Gabano E. Prediction of logP for Pt(II) and Pt(IV) complexes: Comparison of statistical and quantum-chemistry based approaches. J Inorg Biochem 2016; 156:1-13. [PMID: 26717258 DOI: 10.1016/j.jinorgbio.2015.12.006] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2015] [Revised: 11/19/2015] [Accepted: 12/09/2015] [Indexed: 01/31/2023]
Abstract
The octanol/water partition coefficient, logP, is one of the most important physico-chemical parameters for the development of new metal-based anticancer drugs with improved pharmacokinetic properties. This study addresses an issue with the absence of publicly available models to predict logP of Pt(IV) complexes. Following data collection and subsequent development of models based on 187 complexes from literature, we validate new and previously published models on a new set of 11 Pt(II) and 35 Pt(IV) complexes, which were kept blind during the model development step. The error of the consensus model, 0.65 for Pt(IV) and 0.37 for Pt(II) complexes, indicates its good accuracy of predictions. The lower accuracy for Pt(IV) complexes was attributed to experimental difficulties with logP measurements for some poorly-soluble compounds. This model was developed using general-purpose descriptors such as extended functional groups, molecular fragments and E-state indices. Surprisingly, models based on quantum-chemistry calculations provided lower prediction accuracy. We also found that all the developed models strongly overestimate logP values for the three complexes measured in the presence of DMSO. Considering that DMSO is frequently used as a solvent to store chemicals, its effect should not be overlooked when logP measurements by means of the shake flask method are performed. The final models are freely available at http://ochem.eu/article/76903.
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Affiliation(s)
- Igor V Tetko
- Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Institute of Structural Biology, Ingolstaedter Landstrasse 1, b. 60w, D-85764 Neuherberg, Germany; BigChem GmbH, Ingolstaedter Landstrasse 1, b. 60w, D-85764 Neuherberg, Germany.
| | - Hristo P Varbanov
- Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland; Institute of Inorganic Chemistry, University of Vienna, Waehringer Strasse 42, A-1090 Vienna, Austria
| | - Mathea S Galanski
- Institute of Inorganic Chemistry, University of Vienna, Waehringer Strasse 42, A-1090 Vienna, Austria
| | - Mona Talmaciu
- School of Chemistry, Cardiff University, Park Place, Cardiff CF10 3AT, UK; «Iuliu Haţieganu» University of Medicine and Pharmacy, Faculty of Pharmacy, Analytical Chemistry Department, Cluj-Napoca, Romania
| | - James A Platts
- School of Chemistry, Cardiff University, Park Place, Cardiff CF10 3AT, UK
| | - Mauro Ravera
- Dipartimento di Scienze e Innovazione Tecnologica, Università del Piemonte Orientale, Viale Teresa Michel 11, 15121 Alessandria, Italy
| | - Elisabetta Gabano
- Dipartimento di Scienze e Innovazione Tecnologica, Università del Piemonte Orientale, Viale Teresa Michel 11, 15121 Alessandria, Italy
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Tetko IV, M. Lowe D, Williams AJ. The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS. J Cheminform 2016; 8:2. [PMID: 26807157 PMCID: PMC4724158 DOI: 10.1186/s13321-016-0113-y] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Accepted: 01/08/2016] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Melting point (MP) is an important property in regards to the solubility of chemical compounds. Its prediction from chemical structure remains a highly challenging task for quantitative structure-activity relationship studies. Success in this area of research critically depends on the availability of high quality MP data as well as accurate chemical structure representations in order to develop models. Currently, available datasets for MP predictions have been limited to around 50k molecules while lots more data are routinely generated following the synthesis of novel materials. Significant amounts of MP data are freely available within the patent literature and, if it were available in the appropriate form, could potentially be used to develop predictive models. RESULTS We have developed a pipeline for the automated extraction and annotation of chemical data from published PATENTS. Almost 300,000 data points have been collected and used to develop models to predict melting and pyrolysis (decomposition) points using tools available on the OCHEM modeling platform (http://ochem.eu). A number of technical challenges were simultaneously solved to develop models based on these data. These included the handing of sparse data matrices with >200,000,000,000 entries and parallel calculations using 32 × 6 cores per task using 13 descriptor sets totaling more than 700,000 descriptors. We showed that models developed using data collected from PATENTS had similar or better prediction accuracy compared to the highly curated data used in previous publications. The separation of data for chemicals that decomposed rather than melting, from compounds that did undergo a normal melting transition, was performed and models for both pyrolysis and MPs were developed. The accuracy of the consensus MP models for molecules from the drug-like region of chemical space was similar to their estimated experimental accuracy, 32 °C. Last but not least, important structural features related to the pyrolysis of chemicals were identified, and a model to predict whether a compound will decompose instead of melting was developed. CONCLUSIONS We have shown that automated tools for the analysis of chemical information have reached a mature stage allowing for the extraction and collection of high quality data to enable the development of structure-activity relationship models. The developed models and data are publicly available at http://ochem.eu/article/99826.
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Affiliation(s)
- Igor V. Tetko
- />Institute of Structural Biology, Helmholtz Zentrum München für Gesundheit und Umwelt (HMGU), Ingolstädter Landstraße 1, b. 60w, 85764 Neuherberg, Germany
- />BigChem GmbH, 85764 Neuherberg, Germany
| | - Daniel M. Lowe
- />NextMove Software Limited, Innovation Centre (Unit 23), Cambridge Science Park, Cambridge, CB4 0EY UK
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Salmina ES, Haider N, Tetko IV. Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds. Molecules 2015; 21:E1. [PMID: 26703557 PMCID: PMC6273096 DOI: 10.3390/molecules21010001] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Revised: 12/09/2015] [Accepted: 12/15/2015] [Indexed: 11/16/2022] Open
Abstract
The article describes a classification system termed “extended functional groups” (EFG), which are an extension of a set previously used by the CheckMol software, that covers in addition heterocyclic compound classes and periodic table groups. The functional groups are defined as SMARTS patterns and are available as part of the ToxAlerts tool (http://ochem.eu/alerts) of the On-line CHEmical database and Modeling (OCHEM) environment platform. The article describes the motivation and the main ideas behind this extension and demonstrates that EFG can be efficiently used to develop and interpret structure-activity relationship models.
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Affiliation(s)
- Elena S Salmina
- Institute for Organic Chemistry, Technical University Bergakademie Freiberg, Leipziger Str. 29, Freiberg D-09596, Germany.
| | - Norbert Haider
- Department of Pharmaceutical Chemistry, University of Vienna, Althanstraße 14, Vienna A-1090, Austria.
| | - Igor V Tetko
- Institute of Structural Biology, Helmholtz Zentrum München-Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, b. 60w, Neuherberg D-85764, Germany.
- BigChem GmbH, Ingolstädter Landstraße 1, b. 60w, Neuherberg D-85764, Germany.
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Tetko IV, Sushko Y, Novotarskyi S, Patiny L, Kondratov I, Petrenko AE, Charochkina L, Asiri AM. How accurately can we predict the melting points of drug-like compounds? J Chem Inf Model 2014; 54:3320-9. [PMID: 25489863 PMCID: PMC4702524 DOI: 10.1021/ci5005288] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
This article contributes a highly accurate model for predicting the melting points (MPs) of medicinal chemistry compounds. The model was developed using the largest published data set, comprising more than 47k compounds. The distributions of MPs in drug-like and drug lead sets showed that >90% of molecules melt within [50,250]°C. The final model calculated an RMSE of less than 33 °C for molecules from this temperature interval, which is the most important for medicinal chemistry users. This performance was achieved using a consensus model that performed calculations to a significantly higher accuracy than the individual models. We found that compounds with reactive and unstable groups were overrepresented among outlying compounds. These compounds could decompose during storage or measurement, thus introducing experimental errors. While filtering the data by removing outliers generally increased the accuracy of individual models, it did not significantly affect the results of the consensus models. Three analyzed distance to models did not allow us to flag molecules, which had MP values fell outside the applicability domain of the model. We believe that this negative result and the public availability of data from this article will encourage future studies to develop better approaches to define the applicability domain of models. The final model, MP data, and identified reactive groups are available online at http://ochem.eu/article/55638.
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Affiliation(s)
- Igor V Tetko
- Helmholtz-Zentrum München - German Research Centre for Environmental Health (GmbH), Institute of Structural Biology , Munich 85764, Germany
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Liu Z, Zheng M, Yan X, Gu Q, Gasteiger J, Tijhuis J, Maas P, Li J, Xu J. ChemStable: a web server for rule-embedded naïve Bayesian learning approach to predict compound stability. J Comput Aided Mol Des 2014; 28:941-50. [PMID: 25031075 DOI: 10.1007/s10822-014-9778-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2014] [Accepted: 07/09/2014] [Indexed: 11/26/2022]
Abstract
Predicting compound chemical stability is important because unstable compounds can lead to either false positive or to false negative conclusions in bioassays. Experimental data (COMDECOM) measured from DMSO/H2O solutions stored at 50 °C for 105 days were used to predicted stability by applying rule-embedded naïve Bayesian learning, based upon atom center fragment (ACF) features. To build the naïve Bayesian classifier, we derived ACF features from 9,746 compounds in the COMDECOM dataset. By recursively applying naïve Bayesian learning from the data set, each ACF is assigned with an expected stable probability (p(s)) and an unstable probability (p(uns)). 13,340 ACFs, together with their p(s) and p(uns) data, were stored in a knowledge base for use by the Bayesian classifier. For a given compound, its ACFs were derived from its structure connection table with the same protocol used to drive ACFs from the training data. Then, the Bayesian classifier assigned p(s) and p(uns) values to the compound ACFs by a structural pattern recognition algorithm, which was implemented in-house. Compound instability is calculated, with Bayes' theorem, based upon the p(s) and p(uns) values of the compound ACFs. We were able to achieve performance with an AUC value of 84% and a tenfold cross validation accuracy of 76.5%. To reduce false negatives, a rule-based approach has been embedded in the classifier. The rule-based module allows the program to improve its predictivity by expanding its compound instability knowledge base, thus further reducing the possibility of false negatives. To our knowledge, this is the first in silico prediction service for the prediction of the stabilities of organic compounds.
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Affiliation(s)
- Zhihong Liu
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-sen University, 132 East Circle at University City, Guangzhou, 510006, China
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Misiti TM, Tezel U, Pavlostathis SG. Effect of alkyl side chain location and cyclicity on the aerobic biotransformation of naphthenic acids. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2014; 48:7909-7917. [PMID: 24941126 DOI: 10.1021/es501058q] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Aerobic biodegradation of naphthenic acids is of importance to the oil industry for the long-term management and environmental impact of process water and wastewater. The effect of structure, particularly the location of the alkyl side chain as well as cyclicity, on the aerobic biotransformation of 10 model naphthenic acids (NAs) was investigated. Using an aerobic, mixed culture, enriched with a commercial NA mixture (NA sodium salt; TCI Chemicals), batch biotransformation assays were conducted with individual model NAs, including eight 8-carbon isomers. It was shown that NAs with a quaternary carbon at the α- or β-position or a tertiary carbon at the β- and/or β'-position are recalcitrant or have limited biodegradability. In addition, branched NAs exhibited lag periods and lower degradation rates than nonbranched or simple cyclic NAs. Two NA isomers used in a closed bottle, aerobic biodegradation assay were mineralized, while 21 and 35% of the parent compound carbon was incorporated into the biomass. The NA biodegradation probability estimated by two widely used models (BIOWIN 2 and 6) and a recently developed model (OCHEM) was compared to the biodegradability of the 10 model NAs tested in this study as well as other related NAs. The biodegradation probability estimated by the OCHEM model agreed best with the experimental data and was best correlated with the measured NA biodegradation rate.
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Affiliation(s)
- Teresa M Misiti
- School of Civil and Environmental Engineering, Georgia Institute of Technology , Atlanta, Georgia 30332-0512, United States
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