Development of a quantitative structure--property relationship model for estimating normal boiling points of small multifunctional organic molecules

Computer-assisted quantitative structure-property relationship techniques are applied in the development of a robust and accurate model of normal boiling points (boiling at 760 mmHg) for a very diverse set of 268 small organic molecules. Most of the molecules included in this study contain two or more functional groups. The final model yields a tight fit to the training set data (R2 = 0.963), with a fit error of 6.5%. More importantly, the model is also shown to perform well in external prediction. The mean prediction error for boiling points for a 78-member external test set was 12.3 degrees C, or 8.3%. A detailed analysis of the small number of compounds that were either outliers or not well predicted illustrates areas for potential improvement of the methodology used.     

A new method of radioactivation analysis based on the quantitative isotope dilution principle

The simple quantitative isotope dilution method proposed, allows the errors due to flux fluctuation and self-shielding, normal to ordinary radioactivation analysis, to be avoided. Three different methods are discussed. The most satisfactory method from a theoretical viewpoint involves division of the irradiated sample into two parts; in one part, an amount m is extracted and the radioactivity a is measured. An amount of carrier M is added to the other part, an amount m is again extracted and its radioactivity a' is measured. The unknown amount M_x is then calculated from (a/a') I = M/Mx. A method for the determination of traces of copper and silver in metallic tin and zinc based on this principle is described.     

Variable selection in multivariate calibration based on clustering of variable concept

Recently we have proposed a new variable selection algorithm, based on clustering of variable concept (CLoVA) in classification problem. With the same idea, this new concept has been applied to a regression problem and then the obtained results have been compared with conventional variable selection strategies for PLS. The basic idea behind the clustering of variable is that, the instrument channels are clustered into different clusters via clustering algorithms. Then, the spectral data of each cluster are subjected to PLS regression. Different real data sets (Cargill corn, Biscuit dough, ACE QSAR, Soy, and Tablet) have been used to evaluate the influence of the clustering of variables on the prediction performances of PLS. Almost in the all cases, the statistical parameter especially in prediction error shows the superiority of CLoVA-PLS respect to other variable selection strategies. Finally the synergy clustering of variable (sCLoVA-PLS), which is used the combination of cluster, has been proposed as an efficient and modification of CLoVA algorithm. The obtained statistical parameter indicates that variable clustering can split useful part from redundant ones, and then based on informative cluster; stable model can be reached.     

A generalization based on quantitative inadequacy of Archimedes principle in balloon experiments

One consequence of Archimedes' principle is that the mass which a balloon supports in a fluid is independent of the shape of the balloon and depends only upon its volume. For air-filled floating balloons in water some deviations from this result have been observed in first-stage experiments. The dependence of mass on the shape of the balloon which is supported in water has been clearly observed in various observations. It is evident from the first-stage experiments that for floating balloons the principle is only true for particular shapes. The deviations from the principle and contradictions can be explained if the definition of the principle is empirically modified i.e. it is assumed that the upthrust experienced by body is proportional to the weight of the fluid displaced. The constant of proportionality also accounts for shape of body and other relevant factors that were not accounted for by the principle.     

A bootstrapping soft shrinkage approach for variable selection in chemical modeling

In this study, a new variable selection method called bootstrapping soft shrinkage (BOSS) method is developed. It is derived from the idea of weighted bootstrap sampling (WBS) and model population analysis (MPA). The weights of variables are determined based on the absolute values of regression coefficients. WBS is applied according to the weights to generate sub-models and MPA is used to analyze the sub-models to update weights for variables. The optimization procedure follows the rule of soft shrinkage, in which less important variables are not eliminated directly but are assigned smaller weights. The algorithm runs iteratively and terminates until the number of variables reaches one. The optimal variable set with the lowest root mean squared error of cross-validation (RMSECV) is selected. The method was tested on three groups of near infrared (NIR) spectroscopic datasets, i.e. corn datasets, diesel fuels datasets and soy datasets. Three high performing variable selection methods, i.e. Monte Carlo uninformative variable elimination (MCUVE), competitive adaptive reweighted sampling (CARS) and genetic algorithm partial least squares (GA-PLS) are used for comparison. The results show that BOSS is promising with improved prediction performance. The Matlab codes for implementing BOSS are freely available on the website: http://www.mathworks.com/matlabcentral/fileexchange/52770-boss.     

Variable interaction network based variable selection for multivariate calibration

Multivariate calibration problems often involve the identification of a meaningful subset of variables, from a vast number of variables for better prediction of output variables. A new graph theoretic method based on partial correlations (variable interaction network—VIN) is proposed. Many well studied representative calibration datasets spanning different application domains are selected for investigating the performance. Partial least squares (PLS) regression models combined with variable selection techniques are employed for benchmarking the performance. Subsets of variables with different number of variables are retained for the final analysis after VIN selection and progressive prediction accuracies are used for comparison. VIN-PLS results show significant improvement in prediction efficiencies and variable subset optimization. Improvement of up to 45% over existing methods with significantly fewer variables is achieved using the new method. Advantages of VIN based variable selection are highlighted.     

Novel statistical approach for primary high-throughput screening hit selection

The standard activity threshold-based method (the "top X" approach), currently widely used in the high-throughput screening (HTS) data analysis, is ineffective at identifying good-quality hits. We have proposed a novel knowledge-based statistical approach, driven by the hidden structure-activity relationship (SAR) within a screening library, for primary hit selection. Application to an in-house ultrahigh-throughput screening (uHTS) campaign has demonstrated it can directly identify active scaffolds containing valuable SAR information with a greatly improved confirmation rate compared to the standard "top X" method (from 55% to 85%). This approach may help produce high-quality leads and expedite the hit-to-lead process in drug discovery.     

Three-dimensional quantitative structure--property relationship (3D-QSPR) models for prediction of thermodynamic properties of polychlorinated biphenyls (PCBs): enthalpy of sublimation

Three-dimensional quantitative structure--property relationship (3D-QSPR) models have been constructed using comparative molecular field analysis (CoMFA) to correlate the sublimation enthalpies at 298.15 K of a series of polychlorinated biphenyls (PCBs) with their CoMFA-calculated physicochemical properties. Various alignment schemes, such as atom fit, as is, and inertial were employed in this study. Separate CoMFA models were developed using different partial charge formalisms, namely, electrostatic potential (ESP) and Gasteiger-Marsili (GM) charges. Among the four different CoMFA models constructed for sublimation enthalpy (Delta(sub)H(m)(298.15 K)), the model that combined atom fit alignment and ESP charges yielded the greatest self-consistency (r(2) = 0.976) and internal predictive ability (r(cv)(2) = 0.750). This CoMFA model was used to predict Delta(sub)H(m)(298.15 K) of PCBs for which the corresponding experimental values are unavailable in the literature.     

Novel approach to 6-alkenyl-substituted pyridoxine derivatives based on the Heck reaction

A new synthetic approach to 6-alkenyl-substituted pyridoxine derivatives was developed based on the Heck reaction. The reaction, which was catalyzed using a mixture of Pd(OAc)₂, (o-Tol)₃P and Bu₃N as a base, led to seven new 6-alkenyl pyridoxine derivatives. When acrylic acid was used the products of decarboxylation and dimerization were formed.     

Portable paper‐based device for quantitative colorimetric assays relying on light reflectance principle

This paper presents a novel paper‐based analytical device based on the colorimetric paper assays through its light reflectance. The device is portable, low cost (<20 dollars), and lightweight (only 176 g) that is available to assess the cost‐effectiveness and appropriateness of the original health care or on‐site detection information. Based on the light reflectance principle, the signal can be obtained directly, stably and user‐friendly in our device. We demonstrated the utility and broad applicability of this technique with measurements of different biological and pollution target samples (BSA, glucose, Fe, and nitrite). Moreover, the real samples of Fe (II) and nitrite in the local tap water were successfully analyzed, and compared with the standard UV absorption method, the quantitative results showed good performance, reproducibility, and reliability. This device could provide quantitative information very conveniently and show great potential to broad fields of resource‐limited analysis, medical diagnostics, and on‐site environmental detection.     

Rapid and reliable spectral variable selection for statistical calibrations based on PLS-regression vector choices

The optimum number of spectral variables necessary for analytical spectroscopy is still a subject of debate. For sensor applications using miniaturized instrumentation, a small set of significant wavelengths with robust predictive performance is especially appreciated. A fast procedure is proposed, based on pairwise selection of spectral variables suggested by the weights of the optimum PLS-regression vector. The performance of multiple linear regression models based on such choices was similar to, and sometimes improved on full spectrum based modeling. Several examples from clinical studies of blood substrate assays using attenuated total reflection infrared spectroscopy of biofluids are presented. Received: 5 December 1996 / Revised: 13 May 1997 / Accepted: 22 May 1997     

Molecular design of anticancer drug leads based on three-dimensional quantitative structure-activity relationship

Heat shock protein 90 (Hsp90) takes part in the developments of several cancers. Novobiocin, a typically C-terminal inhibitor for Hsp90, will probably used as an important anticancer drug in the future. In this work, we explored the valuable information and designed new novobiocin derivatives based on a three-dimensional quantitative structure-activity relationship (3D QSAR). The comparative molecular field analysis and comparative molecular similarity indices analysis models with high predictive capability were established, and their reliabilities are supported by the statistical parameters. Based on the several important influence factors obtained from these models, six new novobiocin derivatives with higher inhibitory activities were designed and confirmed by the molecular simulation with our models, which provide the potential anticancer drug leads for further research.