| 1. |
- Benfenati, E., et al.
(författare)
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A large comparison of integrated SAR/QSAR models of the Ames test for mutagenicity($)
- 2018
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Ingår i: SAR and QSAR in environmental research (Print). - : Taylor & Francis. - 1062-936X .- 1029-046X. ; 29:8, s. 591-611
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Tidskriftsartikel (refereegranskat)abstract
- Results from the Ames test are the first outcome considered to assess the possible mutagenicity of substances. Many QSAR models and structural alerts are available to predict this endpoint. From a regulatory point of view, the recommendation from international authorities is to consider the predictions of more than one model and to combine results in order to develop conclusions about the mutagenicity risk posed by chemicals. However, the results of those models are often conflicting, and the existing inconsistency in the predictions requires intelligent strategies to integrate them. In our study, we evaluated different strategies for combining results of models for Ames mutagenicity, starting from a set of 10 diverse individual models, each built on a dataset of around 6000 compounds. The novelty of our study is that we collected a much larger set of about 18,000 compounds and used the new data to build a family of integrated models. These integrations used probabilistic approaches, decision theory, machine learning, and voting strategies in the integration scheme. Results are discussed considering balanced or conservative perspectives, regarding the possible uses for different purposes, including screening of large collection of substances for prioritization.
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| 2. |
- Bolelli, K., et al.
(författare)
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Synthesis and activity mechanism of some novel 2-substituted benzothiazoles as hGSTP1-1 enzyme inhibitors
- 2017
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Ingår i: SAR and QSAR in environmental research (Print). - 1062-936X .- 1029-046X. ; 28:11, s. 927-940
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Tidskriftsartikel (refereegranskat)abstract
- Human GSTP1-1 is one of the most important proteins, which overexpresses in a large number of human tumours and is involved in the development of resistance to several anticancer drugs. So, it has become an important target in cancer treatment. In this study, 12 benzothiazole derivatives were synthesized and screened for their in vitro inhibitory activity for hGSTP1-1. Among these compounds, two of them (compounds #2 and #5) have been found to be the leads when compared with the reference drug etoposide. In order to analyse the structure-activity relationships (SARs) and to investigate the binding side interactions of the observed lead compounds, a HipHop pharmacophore model was generated and the molecular docking studies were performed by using CDocker method. In conclusion, it is observed that the lead compounds #2 and #5 possessed inhibitory activity on the hGSTP1-1 by binding to the H-site as a substrate in which the para position of the phenyl ring of the benzamide moiety on the benzothiazole ring is important. Substitution at this position with a hydrophobic group that reduces the electron density at the phenyl ring is required for the interaction with the H side active residue Tyr108.
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| 3. |
- Eriksson, Lennart, et al.
(författare)
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Three-block bi-focal PLS (3BIF-PLS) and its application in QSAR
- 2004
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Ingår i: SAR and QSAR in Environmental Research. - : Informa UK Limited. - 1062-936X .- 1029-046X. ; 15:5 & 6, s. 481-99
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Tidskriftsartikel (refereegranskat)abstract
- When X and Y are multivariate, the two-block partial least squares (PLS) method is often used. In this paper, we outline an extension addressing a special case of the three-block (X/Y/Z) problem, where Z sits "under" Y. We have called this approach three-block bi-focal PLS (3BIF-PLS). It views the X/Y relationship as the dominant problem, and seeks to use the additional information in Z in order to improve the interpretation of the Y-part of the X/Y association. Two data sets are used to illustrate 3BIF-PLS. Example I relates to single point mutants of haloalkane dehalogenase from Sphingomonas paucimobilis UT26 and their ability to transform halogenated hydrocarbons, some of which are found as organic pollutants in soil. Example II deals with soil remediation capability of bacteria. Whole bacterial communities are monitored over time using "DNA-fingerprinting" technology to see how pollution affects population composition. Since the data sets are large, hierarchical multivariate modelling is invoked to compress data prior to 3BIF-PLS analysis. It is concluded that the 3BIF-PLS approach works well. The paper contains a discussion of pros and cons of the method, and hints at further developmental opportunities.
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| 4. |
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| 5. |
- Larsson, Malin, et al.
(författare)
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On the use of electronic descriptors for QSAR modelling of PCDDs, PCDFs and dioxin-like PCBs£ :
- 2013
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Ingår i: SAR and QSAR in environmental research (Print). - : Taylor & Francis Group. - 1062-936X .- 1029-046X. ; 24:6, s. 461-479
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Tidskriftsartikel (refereegranskat)abstract
- The electronic properties of 29 polychlorinated dibenzo-p-dioxins and dibenzofurans and dioxin-like polychlorinated biphenyls that have been included in the toxic equivalency factor system have been investigated and used to derive quantum mechanical (QM) chemical descriptors for QSAR modelling. Their utility in this context was investigated alongside descriptors based on ultraviolet absorption data and traditional 2D descriptors including log Kow, polarizability, molecular surface properties, van der Waals volume and selected connectivity indices. The QM descriptors were calculated using the semi-empirical AM1 method and the density functional theory method B3-LYP/6-31G(∗∗). Atom-specific and molecular quantum chemical descriptors were calculated to compare the electronic properties of dioxin-like compounds regardless of their chemical class, with particular emphasis on the lateral positions. Multivariate analysis revealed differences between the chemical classes in terms of their electronic properties and also highlighted differences between congeners. The results obtained demonstrated the importance of considering molecular orbital energies, but also indicated that the ratios of the coefficients of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) at the lateral carbons were important. In addition, the digitalized UV spectra contained chemical information that provided crucial insights into dioxin-like activity.
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| 6. |
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| 7. |
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| 8. |
- Norinder, Ulf, 1956-, et al.
(författare)
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Conformal prediction of HDAC inhibitors
- 2019
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Ingår i: SAR and QSAR in environmental research (Print). - : Taylor & Francis. - 1062-936X .- 1029-046X. ; 30:4, s. 265-277
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Tidskriftsartikel (refereegranskat)abstract
- The growing interest in epigenetic probes and drug discovery, as revealed by several epigenetic drugs in clinical use or in the lineup of the drug development pipeline, is boosting the generation of screening data. In order to maximize the use of structure-activity relationships there is a clear need to develop robust and accurate models to understand the underlying structure-activity relationship. Similarly, accurate models should be able to guide the rational screening of compound libraries. Herein we introduce a novel approach for epigenetic quantitative structure-activity relationship (QSAR) modelling using conformal prediction. As a case study, we discuss the development of models for 11 sets of inhibitors of histone deacetylases (HDACs), which are one of the major epigenetic target families that have been screened. It was found that all derived models, for every HDAC endpoint and all three significance levels, are valid with respect to predictions for the external test sets as well as the internal validation of the corresponding training sets. Furthermore, the efficiencies for the predictions are above 80% for most data sets and above 90% for four data sets at different significant levels. The findings of this work encourage prospective applications of conformal prediction for other epigenetic target data sets.
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| 9. |
- Norinder, Ulf, 1956-, et al.
(författare)
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Conformal prediction to define applicability domain : A case study on predicting ER and AR binding
- 2016
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Ingår i: SAR and QSAR in environmental research (Print). - : Taylor & Francis. - 1062-936X .- 1029-046X. ; 27:4, s. 303-316
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Tidskriftsartikel (refereegranskat)abstract
- A fundamental element when deriving a robust and predictive in silico model is not only the statistical quality of the model in question but, equally important, the estimate of its predictive boundaries. This work presents a new method, conformal prediction, for applicability domain estimation in the field of endocrine disruptors. The method is applied to binders and non-binders related to the oestrogen and androgen receptors. Ensembles of decision trees are used as statistical method and three different sets (dragon, rdkit and signature fingerprints) are investigated as chemical descriptors. The conformal prediction method results in valid models where there is an excellent balance in quality between the internally validated training set and the corresponding external test set, both in terms of validity and with respect to sensitivity and specificity. With this method the level of confidence can be readily altered by the user and the consequences thereof immediately inspected. Furthermore, the predictive boundaries for the derived models are rigorously defined by using the conformal prediction framework, thus no ambiguity exists as to the level of similarity needed for new compounds to be in or out of the predictive boundaries of the derived models where reliable predictions can be expected.
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| 10. |
- Tysklind, Mats, et al.
(författare)
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Selection of polychlorinated biphenyls for use in quantitative structure-activity modelling
- 1995
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Ingår i: SAR and QSAR in environmental research (Print). - : Informa UK Limited. - 1062-936X .- 1029-046X. ; 4:1, s. 11-19
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Tidskriftsartikel (refereegranskat)abstract
- By characterizing the 154 tetra- through heptachlorinated biphenyl (PCB) congeners with a multitude of physico-chemical descriptors, a model representing chemical similarities and differences is achieved. The multivariate characterization of the PCBs was based on 47 physico-chemical descriptor variables, which were summarised by using principal component analysis (PCA). By applying statistical design to the orthogonal scores from the PCA, a 24-factorial design was used to select a set of 16 congeners. In addition, four congeners were added to provide information about the interior region of the chemical domain of PCBs. This set of 20 structurally different congeners is suggested to be used in future quantitative structure-activity relationships (QSARs) for screening of the toxicological and biochemical effects of the PCBs.
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| 11. |
- Öberg, Tomas, 1956-
(författare)
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A general structure-property relationship to predict the enthalpy of vaporisation at ambient temperatures.
- 2007
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Ingår i: SAR and QSAR in environmental research (Print). - Reading, Berkshire, Great Britain : Gordon and Breach Science Publishers. - 1062-936X .- 1029-046X. ; 18:1-2, s. 127-139
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Tidskriftsartikel (refereegranskat)abstract
- The vapour pressure is the most important property of an anthropogenic organic compound in determining its partitioning between the atmosphere and the other environmental media. The enthalpy of vaporisation quantifies the temperature dependence of the vapour pressure and its value around 298 K is needed for environmental modelling. The enthalpy of vaporisation can be determined by different experimental methods, but estimation methods are needed to extend the current database and several approaches are available from the literature. However, these methods have limitations, such as a need for other experimental results as input data, a limited applicability domain, a lack of domain definition, and a lack of predictive validation. Here we have attempted to develop a quantitative structure-property relationship (QSPR) that has general applicability and is thoroughly validated. Enthalpies of vaporisation at 298 K were collected from the literature for 1835 pure compounds. The three-dimensional (3D) structures were optimised and each compound was described by a set of computationally derived descriptors. The compounds were randomly assigned into a calibration set and a prediction set. Partial least squares regression (PLSR) was used to estimate a low-dimensional QSPR model with 12 latent variables. The predictive performance of this model, within the domain of application, was estimated at n=560, q2Ext=0.968 and s=0.028 (log transformed values). The QSPR model was subsequently applied to a database of 100,000+ structures, after a similar 3D optimisation and descriptor generation. Reliable predictions can be reported for compounds within the previously defined applicability domain.
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