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Panagiotopoulos A, Kalyvianaki K, Notas G, Pirintsos SA, Castanas E, Kampa M. New Antagonists of the Membrane Androgen Receptor OXER1 from the ZINC Natural Product Database. ACS OMEGA 2021; 6:29664-29674. [PMID: 34778638 PMCID: PMC8582029 DOI: 10.1021/acsomega.1c04027] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/13/2021] [Indexed: 06/13/2023]
Abstract
OXER1 (oxoeicosanoid receptor 1) was deorphanized in 1993 and found to be the specific receptor for the arachidonic acid metabolite 5-oxo-ETE. Recently, we have reported that androgen binds to this receptor also, being a membrane androgen receptor, triggering a number of its membrane-mediated actions (cell migration, apoptosis, cell proliferation, Ca2+ movements). In addition, our previous work suggested that a number of natural monomeric and oligomeric polyphenols interact with OXER1, acting similar to testosterone. Here, we interrogated the natural product chemical space and identified nine polyphenolic molecules with interesting in silico pharmacological activities as putative OXER1 antagonists. The molecule with the best pharmacokinetic-pharmacodynamic properties (ZINC15959779) was purchased and tested on OXER1, in prostate cancer cell cultures. It showed that it has actions similar to those of testosterone in inhibiting cAMP, while it had no action in intracellular Ca2+ mobilization or actin cytoskeleton rearrangement/migration. These results are discussed under the prism of structure-activity relationships and in silico models of the OXER1 binding groove. We suggest that these compounds, together with the previously reported (poly)phenolic compounds, can be lead structures for the exploration of the anti-inflammatory and antiproliferative effects of OXER1 antagonists.
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Affiliation(s)
| | - Konstantina Kalyvianaki
- Laboratory
of Experimental Endocrinology, School of Medicine, University of Crete, Heraklion 715 00, Greece
| | - George Notas
- Laboratory
of Experimental Endocrinology, School of Medicine, University of Crete, Heraklion 715 00, Greece
| | - Stergios A. Pirintsos
- Department
of Biology, School of Science and Technology, University of Crete, Heraklion 71013, Greece
- Botanical
Garden, University of Crete, Rethymnon 700 13, Greece
| | - Elias Castanas
- Laboratory
of Experimental Endocrinology, School of Medicine, University of Crete, Heraklion 715 00, Greece
| | - Marilena Kampa
- Laboratory
of Experimental Endocrinology, School of Medicine, University of Crete, Heraklion 715 00, Greece
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2
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Stanton DT, Baker JR, McCluskey A, Paula S. Development and interpretation of a QSAR model for in vitro breast cancer (MCF-7) cytotoxicity of 2-phenylacrylonitriles. J Comput Aided Mol Des 2021; 35:613-628. [PMID: 33945106 PMCID: PMC8093599 DOI: 10.1007/s10822-021-00387-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 04/25/2021] [Indexed: 12/04/2022]
Abstract
The Arylhydrocarbon Receptor (AhR), a member of the Per-ARNT-SIM transcription factor family, has been as a potential new target to treat breast cancer sufferers. A series of 2-phenylacrylonitriles targeting AhR has been developed that have shown promising and selective activity against cancerous cell lines while sparing normal non-cancerous cells. A quantitative structure–activity relationship (QSAR) modeling approach was pursued in order to generate a predictive model for cytotoxicity to support ongoing synthetic activities and provide important structure-activity information for new structure design. Recent work conducted by us has identified a number of compounds that exhibited false positive cytotoxicity values in the standard MTT assay. This work describes a good quality model that not only predicts the activity of compounds in the MCF-7 breast cancer cell line, but was also able to identify structures that subsequently gave false positive values in the MTT assay by identifying compounds with aberrant biological behavior. This work not only allows the design of future breast cancer cytotoxic activity in vitro, but allows the avoidance of the synthesis of those compounds anticipated to result in anomalous cytotoxic behavior, greatly enhancing the design of such compounds.
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Affiliation(s)
| | - Jennifer R Baker
- Chemistry, School of Environmental & Life Sciences, The University of Newcastle, University Drive, Callaghan, NSW, 2308, Australia
| | - Adam McCluskey
- Chemistry, School of Environmental & Life Sciences, The University of Newcastle, University Drive, Callaghan, NSW, 2308, Australia
| | - Stefan Paula
- Department of Chemistry, Tschannen Science Center, California State University at Sacramento, 6000 J Street, Sacramento, CA, 95819, USA
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3
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Abstract
At the end of her academic career, the author summarizes the main aspects of QSAR modeling, giving comments and suggestions according to her 23 years' experience in QSAR research on environmental topics. The focus is mainly on Multiple Linear Regression, particularly Ordinary Least Squares, using a Genetic Algorithm for variable selection from various theoretical molecular descriptors, but the comments can be useful also for other QSAR methods. The need for rigorous validation, also external, and for applicability domain check to guarantee predictivity and reliability of QSAR models is particularly highlighted. The commented approach is the “predictive” one, based on chemometrics, and is usefully applied to the prioritization of environmental pollutants. All the discussed points and the author's ideas are implemented in the software QSARINS, as a legacy to the QSAR community.
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Tugcu G, Sipahi H. QSPR modelling of in vitro degradation half-life of acyl glucuronides. Xenobiotica 2018; 49:1007-1014. [DOI: 10.1080/00498254.2018.1527049] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Gulcin Tugcu
- Department of Toxicology, Yeditepe University, Istanbul, Turkey
| | - Hande Sipahi
- Department of Toxicology, Yeditepe University, Istanbul, Turkey
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5
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QSAR of Heterocyclic Compounds in Large Descriptor Spaces. ADVANCES IN HETEROCYCLIC CHEMISTRY 2016. [DOI: 10.1016/bs.aihch.2016.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register]
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6
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Moosus M, Hiob R, Maran U. Quantitative relationship between rate constants and molecular structure descriptors for the gas phase hydrogen abstraction reactions. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2013; 24:501-518. [PMID: 23724929 DOI: 10.1080/1062936x.2013.792869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
The abstraction of hydrogen by general radicals has a wide role in environmental and also in technological processes because it results in reactive free radicals that play a vital role in atmospheric chemistry and also in biochemical processes. In addition to experimental studies, the theoretical modelling of this elementary reaction has been important for understanding and predicting respective rate constants. In this paper, molecular descriptors in the context of a QSAR approach are used to codify the relationship between molecular structure and rate constants. Unique experimental data is collected from the literature for the reaction R(i)• + R(j)H → R(i)H + R(j)•, where R(i)• = H• and R(j)• are diverse radicals. The four-parameter QSAR model (n = 34, r(2) = 0.81, r(2)(CV) = 0.74, r(2)(scr) = 0.12, s(2) = 0.19) is presented for the bimolecular rate constants, accompanied with model diagnostics and analysis of descriptors in the model.
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Affiliation(s)
- Maikki Moosus
- Institute of Chemistry, University of Tartu, Tartu, Estonia
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7
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Piir G, Sild S, Maran U. Comparative analysis of local and consensus quantitative structure-activity relationship approaches for the prediction of bioconcentration factor. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2013; 24:175-199. [PMID: 23410132 DOI: 10.1080/1062936x.2012.762426] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Quantitative structure-activity relationships (QSARs) are broadly classified as global or local, depending on their molecular constitution. Global models use large and diverse training sets covering a wide range of chemical space. Local models focus on smaller structurally or chemically similar subsets that are conventionally selected by human experts or alternatively using clustering analysis. The current study focuses on the comparative analysis of different clustering algorithms (expectation-maximization, K-means and hierarchical) for seven different descriptor sets as structural characteristics and two rule-based approaches to select subsets for designing local QSAR models. A total of 111 local QSAR models are developed for predicting bioconcentration factor. Predictions from local models were compared with corresponding predictions from the global model. The comparison of coefficients of determination (r(2)) and standard deviations for local models with similar subsets from the global model show improved prediction quality in 97% of cases. The descriptor content of derived QSARs is discussed and analyzed. Local QSAR models were further consolidated within the framework of consensus approach. All different consensus approaches increased performance over the global and local models. The consensus approach reduced the number of strongly deviating predictions by evening out prediction errors, which were produced by some local QSARs.
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Affiliation(s)
- G Piir
- Institute of Chemistry, University of Tartu, Tartu, Estonia
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8
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Pogliani L, de Julián-Ortiz JV. Testing selected optimal descriptors with artificial neural networks. RSC Adv 2013. [DOI: 10.1039/c3ra41435c] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
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Ning J, Chen W, Li J, Peng Z, Wang J, Ni Z. Structural and energetic insights into sequence-specific interaction in DNA–drug recognition: development of affinity predictor and analysis of binding selectivity. J Mol Model 2012; 19:1573-82. [DOI: 10.1007/s00894-012-1722-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2012] [Accepted: 12/03/2012] [Indexed: 11/28/2022]
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Hutter MC. Determining the Degree of Randomness of Descriptors in Linear Regression Equations with Respect to the Data Size. J Chem Inf Model 2011; 51:3099-104. [DOI: 10.1021/ci200403j] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Michael C. Hutter
- Center for Bioinformatics, Campus Building E2.1, Saarland University, 66123 Saarbrücken, Germany
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11
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Linear and nonlinear quantitative structure–activity relationship modeling of the HIV-1 reverse transcriptase inhibiting activities of thiocarbamates. Anal Chim Acta 2011; 705:166-73. [DOI: 10.1016/j.aca.2011.04.046] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2010] [Revised: 03/31/2011] [Accepted: 04/21/2011] [Indexed: 11/21/2022]
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12
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Jalali-Heravi M, Mani-Varnosfaderani A. QSAR modelling of integrin antagonists using enhanced Bayesian regularised genetic neural networks. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2011; 22:293-314. [PMID: 21598195 DOI: 10.1080/1062936x.2011.569758] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Bayesian regularised genetic neural network (BRGNN) has been used for modelling the inhibition activity of 141 biphenylalanine derivatives as integrin antagonists. Three local pattern search (PS) methods, simulated annealing and threshold acceptance were combined with BRGNN in the form of a hybrid genetic algorithm (HGA). The results obtained revealed that PS is a suitable method for improving the ability of BRGNN to break out from the local minima. The proposed HGA technique is able to retrieve important variables from complex systems and nonlinear search spaces for optimisation. Two models with 8-3-1 artificial neural network (ANN) architectures were developed for describing α₄β₇ and α₄β₁ modulatory activities of integrin antagonists. Monte Carlo cross-validation was performed to validate the models and Q₂ values of 0.75 and 0.74 were obtained for α₄β₇ and α₄β₁ inhibitory activities, respectively. The scrambling technique was used for sensitivity analysis of descriptors appearing in ANN models. Frequencies of repetition and sensitivity analysis of molecular descriptors revealed that 3D-Morse descriptors are influential factors for describing α₄β₇ inhibitory activity, while in the case of α₄β₁ inhibitory activity, the Randic shape index, the lowest eigenvalue of the Burden matrix and the number of rotatable bonds are important parameters.
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Affiliation(s)
- M Jalali-Heravi
- Department of Chemistry, Sharif University of Technology, Tehran, Iran.
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13
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Liu F, Cao C, Cheng B. A Quantitative Structure-Property Relationship (QSPR) Study of aliphatic alcohols by the method of dividing the molecular structure into substructure. Int J Mol Sci 2011; 12:2448-62. [PMID: 21731451 PMCID: PMC3127127 DOI: 10.3390/ijms12042448] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2011] [Revised: 03/24/2011] [Accepted: 04/06/2011] [Indexed: 11/16/2022] Open
Abstract
A quantitative structure–property relationship (QSPR) analysis of aliphatic alcohols is presented. Four physicochemical properties were studied: boiling point (BP), n-octanol–water partition coefficient (lg POW), water solubility (lg W) and the chromatographic retention indices (RI) on different polar stationary phases. In order to investigate the quantitative structure–property relationship of aliphatic alcohols, the molecular structure ROH is divided into two parts, R and OH to generate structural parameter. It was proposed that the property is affected by three main factors for aliphatic alcohols, alkyl group R, substituted group OH, and interaction between R and OH. On the basis of the polarizability effect index (PEI), previously developed by Cao, the novel molecular polarizability effect index (MPEI) combined with odd-even index (OEI), the sum eigenvalues of bond-connecting matrix (SX1CH) previously developed in our team, were used to predict the property of aliphatic alcohols. The sets of molecular descriptors were derived directly from the structure of the compounds based on graph theory. QSPR models were generated using only calculated descriptors and multiple linear regression techniques. These QSPR models showed high values of multiple correlation coefficient (R > 0.99) and Fisher-ratio statistics. The leave-one-out cross-validation demonstrated the final models to be statistically significant and reliable.
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Affiliation(s)
- Fengping Liu
- School of Chemistry and Chemical Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China; E-Mails: (F.L.); (B.C.)
| | - Chenzhong Cao
- School of Chemistry and Chemical Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China; E-Mails: (F.L.); (B.C.)
- Key Laboratory of Theoretical Chemistry and Molecular Simulation of Ministry of Education, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China
- Author to whom correspondence should be addressed; E-Mail:
| | - Bin Cheng
- School of Chemistry and Chemical Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China; E-Mails: (F.L.); (B.C.)
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14
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Sharma BK, Pilania P, Singh P, Prabhakar YS. A QSAR study on 2-(4-methylpiperazin-1-yl)quinoxalines as human histamine H4 receptor ligands. J Enzyme Inhib Med Chem 2010; 26:412-21. [DOI: 10.3109/14756366.2010.519702] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Brij K. Sharma
- Department of Chemistry, S. K. Government College, Sikar 332 001, Rajasthan, India
| | - Pradeep Pilania
- Department of Chemistry, S. K. Government College, Sikar 332 001, Rajasthan, India
| | - Prithvi Singh
- Department of Chemistry, S. K. Government College, Sikar 332 001, Rajasthan, India
| | - Yenamandra S. Prabhakar
- Medicinal and Process Chemistry Division, Central Drug Research Institute, CSRI, Lucknow 226 001, Uttar Pradesh, India
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15
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Piir G, Sild S, Roncaglioni A, Benfenati E, Maran U. QSAR model for the prediction of bio-concentration factor using aqueous solubility and descriptors considering various electronic effects. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2010; 21:711-729. [PMID: 21120758 DOI: 10.1080/1062936x.2010.528596] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
The in silico modelling of bio-concentration factor (BCF) is of considerable interest in environmental sciences, because it is an accepted indicator for the accumulation potential of chemicals in organisms. Numerous QSAR models have been developed for the BCF, and the majority utilize the octanol/water partition coefficient (log P) to account for the penetration characteristics of the chemicals. The present work used descriptors from a variety of software packages for the development of a multi-linear regression model to estimate BCF. The modelled data set of 473 diverse compounds covers a wide range of log BCF values. In the proposed QSAR model, most of the variation is described by the calculated solubility in water. Other contributing descriptors describe, for instance, hydrophobic surface area, hydrogen bonding and other electronic effects. The model was validated internally by using a variety of statistical approaches. Two external validations were also performed. For the former validation, a subset from the same data source was used. The 2nd external validation was based on an independent data set collected from different resources. All validations showed the consistency of the model. The applicability domain of the model was discussed and described and a thorough outlier analysis was performed.
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Affiliation(s)
- G Piir
- Institute of Chemistry, University of Tartu, Tartu, Estonia
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16
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Katritzky AR, Kuanar M, Slavov S, Hall CD, Karelson M, Kahn I, Dobchev DA. Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction. Chem Rev 2010; 110:5714-89. [DOI: 10.1021/cr900238d] [Citation(s) in RCA: 386] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Alan R. Katritzky
- Center for Heterocyclic Compounds, Department of Chemistry, University of Florida, Gainesville, Florida 32611
| | - Minati Kuanar
- Center for Heterocyclic Compounds, Department of Chemistry, University of Florida, Gainesville, Florida 32611
| | - Svetoslav Slavov
- Center for Heterocyclic Compounds, Department of Chemistry, University of Florida, Gainesville, Florida 32611
| | - C. Dennis Hall
- Center for Heterocyclic Compounds, Department of Chemistry, University of Florida, Gainesville, Florida 32611
| | - Mati Karelson
- Institute of Chemistry, Tallinn University of Technology, Akadeemia tee 15, Tallinn 19086, Estonia, and MolCode, Ltd., Soola 8, Tartu 51013, Estonia
| | - Iiris Kahn
- Institute of Chemistry, Tallinn University of Technology, Akadeemia tee 15, Tallinn 19086, Estonia, and MolCode, Ltd., Soola 8, Tartu 51013, Estonia
| | - Dimitar A. Dobchev
- Institute of Chemistry, Tallinn University of Technology, Akadeemia tee 15, Tallinn 19086, Estonia, and MolCode, Ltd., Soola 8, Tartu 51013, Estonia
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17
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Katritzky AR, Kasemets K, Slavov S, Radzvilovits M, Tämm K, Karelson M. Estimating the toxicities of organic chemicals in activated sludge process. WATER RESEARCH 2010; 44:2451-2460. [PMID: 20153498 DOI: 10.1016/j.watres.2010.01.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2009] [Revised: 01/11/2010] [Accepted: 01/12/2010] [Indexed: 05/28/2023]
Abstract
The experimental logEC50 toxicity values of 104 compounds causing bioluminescent repression of the bacterium strain Pseudomonas isolated from an industrial wastewater were studied. Using the Best Multilinear Regression method implemented in CODESSA PRO, models with up to 8 theoretical descriptors were obtained. Utilizing a rigorous descriptor selection and validation procedure a reliable QSAR model with four parameters was selected as best. The proposed model emphasizes the importance of the halogen atoms presented in each compound, the possibility of H-bond formation and the flexibility and degree of branching of the molecules. As pointed out by many researchers, the contribution of the octanol-water partition coefficient to the explanation of the toxicity effect was also found to be significant. In addition, the model currently proposed was compared to those reported earlier and its advantages were discussed in detail.
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Affiliation(s)
- Alan R Katritzky
- Center for Heterocyclic Compounds, Department of Chemistry, University of Florida, Gainesville, Florida 32611, USA.
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18
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Oliferenko PV, Oliferenko AA, Poda G, Palyulin VA, Zefirov NS, Katritzky AR. New Developments in Hydrogen Bonding Acidity and Basicity of Small Organic Molecules for the Prediction of Physical and ADMET Properties. Part 2. The Universal Solvation Equation. J Chem Inf Model 2009; 49:634-46. [DOI: 10.1021/ci800323q] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Polina V. Oliferenko
- Department of Chemistry, Moscow State University, Moscow, 119992 Russia, Structural & Computational Chemistry Group, Pfizer Global Research & Development, Chesterfield, Missouri 63017, and Department of Chemistry, University of Florida, Gainesville, Florida 32611-7200
| | - Alexander A. Oliferenko
- Department of Chemistry, Moscow State University, Moscow, 119992 Russia, Structural & Computational Chemistry Group, Pfizer Global Research & Development, Chesterfield, Missouri 63017, and Department of Chemistry, University of Florida, Gainesville, Florida 32611-7200
| | - Gennadiy Poda
- Department of Chemistry, Moscow State University, Moscow, 119992 Russia, Structural & Computational Chemistry Group, Pfizer Global Research & Development, Chesterfield, Missouri 63017, and Department of Chemistry, University of Florida, Gainesville, Florida 32611-7200
| | - Vladimir A. Palyulin
- Department of Chemistry, Moscow State University, Moscow, 119992 Russia, Structural & Computational Chemistry Group, Pfizer Global Research & Development, Chesterfield, Missouri 63017, and Department of Chemistry, University of Florida, Gainesville, Florida 32611-7200
| | - Nikolay S. Zefirov
- Department of Chemistry, Moscow State University, Moscow, 119992 Russia, Structural & Computational Chemistry Group, Pfizer Global Research & Development, Chesterfield, Missouri 63017, and Department of Chemistry, University of Florida, Gainesville, Florida 32611-7200
| | - Alan R. Katritzky
- Department of Chemistry, Moscow State University, Moscow, 119992 Russia, Structural & Computational Chemistry Group, Pfizer Global Research & Development, Chesterfield, Missouri 63017, and Department of Chemistry, University of Florida, Gainesville, Florida 32611-7200
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