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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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          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.

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          Most cited references 18

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

          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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              3D-QSAR CoMFA studies on sulfonamide inhibitors of the Rv3588c β-carbonic anhydrase from Mycobacterium tuberculosis and design of not yet synthesized new molecules.

              The human pathogen Mycobacterium tuberculosis contains three β-carbonic anhydrases (CAs, EC in its genome. Inhibition of some of these CAs was shown to modulate the growth of M. tuberculosis. 3D-QSAR Comparative molecular field analyses (CoMFA) were carried out on inhibitors of the enzyme Rv3588c (also denominated mtCA 2). A series of sulfonamides known to inhibit mtCA 2, including some diazenylbenzenesulfonamides, was considered in our study. The predictive ability of the model was assessed using a test set of seven compounds. The best model has demonstrated a good fit having predictive r(2) value of 0.93 and cross-validated coefficient q(2) value as 0.88 in tripos CoMFA region. Our results indicate that the steric and electrostatic factors play a significant role in mtCA 2 inhibition for the investigated compounds. We proposed nine new not yet synthesized mtCA 2 inhibitors, all of them probably with significantly improved anti-Rv3588c inhibitory activity.

                Author and article information

                Tsinghua Science and Technology
                Tsinghua University Press (Xueyuan Building, Tsinghua University, Beijing 100084, China )
                05 September 2018
                : 06
                : 03
                : 289-296 (pp. )
                [ 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@

                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.


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                Figures: 6, Tables: 2, References: 22, Pages: 8
                Research Article


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