Biomedical subjects

Chunsheng Yin

Publications and source records attributed to Chunsheng Yin.

6 recordsLinked to original sources

PubMed2006

Quantitative structure-activity relationship studies on HEPTs by supervised stochastic resonance.

Quantitative structure-activity relationship studies (QSAR) on HEPTs were performed by using a new approach--supervised stochastic resonance (SSR) in this paper. Errors in physicochemical properties have great effects on variable selection and the predictive capability of QSAR models but errors-in-variables were seldom discussed in QSAR. In this paper, based on the theory of stochastic resonance (SR), SSR was proposed and employed to the problem. In SSR, errors and abundant variables were regarded as noise and the relevant descriptors as signals. In the nonlinear systems involved in the SR, the signal and the noise interact harmonically and the signal was consequently enhanced. Therefore, the correlation between the relevant variables and a specified activity of a series molecule was improved by SSR. It is demonstrated that the obtained QSAR models for HEPT analogues by SSR were comparable to those by published methods in their stability and predictivity. SSR is an efficient and promising approach to QSAR studies.

Molecular Structure↗

PubMed2006

Novel distance-based atom-type topological indices DAI for QSPR/QSAR studies of alcohols.

In this work, we propose a distance-based atom-type topological index (DAI) for quantitative structure-property/activity relationship (QSPR/QSAR) studies. The newly constructed index, which codes the structural environment of each atom type in a molecule, can be calculated simply. These atom-type topological indices, along with our recently proposed Lu index, were used to construct QSPR/QSAR models for several representative physical properties and biological activities of several data sets of alcohols with a range of non-hydrogen atoms by using multiple linear regression (MLR) analysis. The efficiency of these indices is verified by high quality QSPR models. The results indicate that the combined use of Lu and DAI indices promises to be a useful method for QSPR/QSAR analysis of complex compounds.

Alcohols↗

PubMed2005

QSPR study on soil sorption coefficient for persistent organic pollutants.

Quantitative structure-property relationship (QSPR) models of soil sorption coefficients for 32 persistent organic pollutants were constructed using our recently introduced Lu index and novel distance-based atom-type DAI topological indices. Using multiple linear regression technique, a 6-variable model was obtained with the correlation coefficient of estimations (R) being 0.95, and the standard error of estimations (s) being 0.23, and the correlation coefficient (R(cv)) and the standard error (s(cv)) in the leave-4-out cross-validation procedure are 0.90 and 0.31, respectively. The results in this study indicate that soil sorption coefficients of POPs are dominated by molecular size while some DAI indices have smaller influence.

Adsorption↗

PubMed2004

Holographic QSAR of selected esters.

The HQSAR (Holographic QSAR) method, which has been recently developed, can offer the ability to rapidly and easily generate QSAR models of high statistical quality and predictive value. HQSAR analysis requires selecting values for parameters that specify the size of the hologram that is to be used, and the size and type of fragment substructures that are to be encoded. The color coding is provided by HQSAR to reflect which molecular fragments may be important contributors to the biological activity. In this work, we studied the quantitative structure activity relationship of selected esters using the HQSAR method. A robust HQSAR model with r(2) (non-cross-validated regression coefficient) of 0.981 and q(2) (cross-validated regression coefficient) of 0.912, was developed after optimizing the fragment size and the hologram length. The color coding analysis, which has rarely been reported before, was done here to explain the outlier successfully.

Animals↗

PubMed2002

Prediction and application in QSPR of aqueous solubility of sulfur-containing aromatic esters using GA-based MLR with quantum descriptors.

Quantitative structure-property relationships (QSPR) were developed using a genetic algorithm (GA)-based variable-selection approach with quantum chemical descriptors derived from AM1-based calculations (MOPAC7.0). With the QSPR models, the aqueous solubility of 71 aromatic sulfur-containing carboxylates, including phenylthio, and phenylsulfonyl carboxylates were efficiently estimated and predicted. Using GA-based multivariate linear regression (MLR) with cross-validation procedure, the most important descriptors were selected from a pool of 28 quantum chemical semi-empirical descriptors, including steric and electronic types, to build QSPR models. The molecular descriptors included molecular surface (SA), charges on carboxyl group (Q(oc)), the magnitude of the difference between E(HOMO) of the solute and ELUMO of water, divided by 100 (E(B)), which were main factors affecting the aqueous solubility of the compounds of interest. The resulted coefficients R and R2 of 0.9571 and 0.9161 and the prediction residual error sum of squares (PRESS) of 13.1768, revealed that it was accurate and reliable for the model to predict the aqueous solubility of the investigated organic compounds. If two outliers were omitted from the dataset, the resulted coefficients R = 0.9619, R2 = 0.9253, and PRESS = 10.3875 were significantly improved. Compared with stepwise regression analysis, the results obtained in this work were better and more reasonable. The best QSPR model were obtained by GA-based MLR. Reasonable mechanisms for aqueous solubility of the sulfur-containing carboxylates were investigated and interpreted.

Algorithms↗

PubMed2002

Structure-activity relationships and response-surface analysis of nitroaromatics toxicity to the yeast (Saccharomyces cerevisiae).

Inhibition of growth of the yeast Saccharomyces cerevisiae (Cmiz, the minimum concentration that produced a clear inhibition zone within 12 h) for 24 nitroaromatic compounds was investigated and a quantitative structure-activity relationship (QSAR) developed based on hydrophobicity expressed as the l-octanol/water partition coefficient in logarithm form, log K(ow), electrophilicity based on the energy of the lowest unoccupied orbital (E(lumo)). All nitrobenzene derivatives exhibited enhanced reactive toxicity than baseline. The toxicities of mono-nitrobenzenes and di-nitrobenzenes were elicited by different mechanisms of toxic action. For mono-nitro-derivatives, both significant log K(ow) based and strong E(lumo)-dependent relationships were observed indicating that their toxicities were affected both by the penetration process and the interaction with target sites of interaction. The toxicities of di-nitrobenzenes were greater than mono-nitrobenzenes and no log K(ow)-dependent but highly significant E(lumo)-based relationship was obtained. This suggests that toxicity of di-nitrobenzenes was highly electrophilic and involved mainly their in vivo electrophilic interaction with biomacromolecules. In an effort to model the elevated toxicity of all nitrobenzenes, a response-surface analysis was performed and this resulted in a highly predictive two-variable QSAR without reference to their exact mechanisms (Cmiz = 0.41 log K(ow) - 0.89 E(lumo) - 0.46, r2 = 0.87, Q2 = 0.86, n = 24).

Nitrobenzenes↗