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      Estimating antiwear properties of esters as potential lubricant- based oils using QSTR models with CoMFA and CoMSIA

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          Abstract

          Comparative molecular field analysis and comparative molecular similarity indices analysis were employed to analyze the antiwear properties of a series of 57 esters as potential lubricant-based oils. Predictive 3D-quantitative structure tribo-ability relationship models were established using the SYBYL multifit molecular alignment rule with a training set and a test set. The optimum models were all shown to be statistically significant with cross-validated coefficients q 2 > 0.5 and conventional coefficients r 2 > 0.9, indicating that the models are sufficiently reliable for activity prediction, and may be useful in the design of novel ester-based oils.

          Most cited references18

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          Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity.

          Gerhard Klebe, Ute Abraham, Thomas Mietzner (1994)
          An alternative approach is reported to compute property fields based on similarity indices of drug molecules that have been brought into a common alignment. The fields of different physicochemical properties use a Gaussian-type distance dependence, and no singularities occur at the atomic positions. Accordingly, no arbitrary definitions of cutoff limits and deficiencies due to different slopes of the fields are encountered. The fields are evaluated by a PLS analysis similar to the CoMFA formalism. Two data sets of steroids binding to the corticosteroid-binding-globulin and thermolysin inhibitors were analyzed in terms of the conventional CoMFA method (Lennard-Jones and Coulomb potential fields) and the new comparative molecular similarity indices analysis (CoMSIA). Models of comparative statistical significance were obtained. Field contribution maps were produced for the different models. Due to cutoff settings in the CoMFA fields and the steepness of the potentials close to the molecular surface, the CoMFA maps are often rather fragmentary and not contiguously connected. This makes their interpretation difficult. The maps obtained by the new CoMSIA approach are superior and easier to interpret. Whereas the CoMFA maps denote regions apart from the molecules where interactions with a putative environment are to be expected, the CoMSIA maps highlight those regions within the area occupied by the ligand skeletons that require a particular physicochemical property important for activity. This is a more significant guide to trace the features that really matter especially with respect to the design of novel compounds.
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            Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA.

            B Bush, R Nachbar (1993)
            Three-dimensional molecular modeling can provide an unlimited number m of structural properties. Comparative Molecular Field Analysis (CoMFA), for example, may calculate thousands of field values for each model structure. When m is large, partial least squares (PLS) is the statistical method of choice for fitting and predicting biological responses. Yet PLS is usually implemented in a property-based fashion which is optimal only for small m. We describe here a sample-based formulation of PLS which can be used to fit any single response (bioactivity). SAMPLS reduces all explanatory data to the pairwise 'distances' among n samples (molecules), or equivalently to an n-by-n covariance matrix C. This matrix, unmodified, can be used to fit all PLS components. Furthermore, SAMPLS will validate the model by modern resampling techniques, at a cost independent of m. We have implemented SAMPLS as a Fortran program and have reproduced conventional and cross-validated PLS analyses of data from two published studies. Full (leave-each-out) cross-validation of a typical CoMFA takes 0.2 CPU s. SAMPLS is thus ideally suited to structure-activity analysis based on CoMFA fields or bonded topology. The sample-distance formulation also relates PLS to methods like cluster analysis and nonlinear mapping, and shows how drastically PLS simplifies the information in CoMFA fields.
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              Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection.

              Alexander Golbraikh, Alexander Tropsha (2002)
              One of the most important characteristics of Quantitative Structure Activity Relashionships (QSAR) models is their predictive power. The latter can be defined as the ability of a model to predict accurately the target property (e.g., biological activity) of compounds that were not used for model development. We suggest that this goal can be achieved by rational division of an experimental SAR dataset into the training and test set, which are used for model development and validation, respectively. Given that all compounds are represented by points in multidimensional descriptor space, we argue that training and test sets must satisfy the following criteria: (i) Representative points of the test set must be close to those of the training set; (ii) Representative points of the training set must be close to representative points of the test set; (iii) Training set must be diverse. For quantitative description of these criteria, we use molecular dataset diversity indices introduced recently (Golbraikh, A., J. Chem. Inf. Comput. Sci., 40 (2000) 414-425). For rational division of a dataset into the training and test sets, we use three closely related sphere-exclusion algorithms. Using several experimental datasets, we demonstrate that QSAR models built and validated with our approach have statistically better predictive power than models generated with either random or activity ranking based selection of the training and test sets. We suggest that rational approaches to the selection of training and test sets based on diversity principles should be used routinely in all QSAR modeling research.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Tsinghua Science and Technology
                Title: Friction
                Publisher: Tsinghua University Press (Xueyuan Building, Tsinghua University, Beijing 100084, China )
                ISSN (Print): 2223-7690
                Publication date (Print): 05 September 2018
                Volume: 06
                Issue: 03
                Pages: 289-296 (pp. )
                Affiliations
                [ 1 ] College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, China
                [ 2 ] School of Chemical and Environmental Engineering, Wuhan Polytechnic University, Wuhan 430023, China
                [ 3 ] College of Pharmacy, South-Central University for Nationalities, Wuhan 430074, China
                Author notes
                * Corresponding author: Xinlei GAO, E-mail: gaoxl0131@ 123456163.com

                Zhan WANG. She received her M.S. degree in food science and engineering in 2002 from Henan University of Technology, and graduated from Huazhong University of Science and Technology in biomedical engineering with PhD degree in 2009. Currently she is an associate professor at Wuhan Polytechnic University. Her research interests include organic chemistry and chemical computing.

                Xinei GAO. She received her M.S. degree in 1996 from Huazhong Normal University in organic chemistry, and graduated from Wuhan Research Institute of Materials Protection in mechanical design and theory with PhD degree in 2006. Currently she is a full professor at Wuhan Polytechnic University, member of Chinese Tribology Association. She is interested in tribology chemistry, chemical computing, and designation of lubricant.

                Article
                Publisher ID: 2223-7690-06-03-289
                DOI: 10.1007/s40544-017-0175-5
                SO-VID: f958c4eb-8115-4e2c-b813-498db1c871cb
                Copyright statement: Copyright @ 2018
                License:

                © The author(s) 2017. This article is published with open access at Springerlink.com

                Electronic Supplementary Material: Supplementary Material (Tables S1 and S2) is available in the online version of this article at https://doi.org/10.1007/ s40544-017-0175-5.

                Open Access: The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                Date received : 06 January 2017
                Date: 03 May 2017
                Date accepted : 19 June 2017
                Page count
                Figures: 6, Tables: 2, References: 22, Pages: 8
                Categories
                Subject: Research Article

                ScienceOpen disciplines: Materials technology,Materials properties,Thin films & surfaces,Mechanical engineering
                Keywords: quantitative structure tribo-ability relationship,lubricant-based oils,antiwear properties
                Data availability:
                ScienceOpen disciplines: Materials technology, Materials properties, Thin films & surfaces, Mechanical engineering
                Keywords: quantitative structure tribo-ability relationship, lubricant-based oils, antiwear properties

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