02302nas a2200253   4500008004100000022001400041245009500055210006900150260011100219300001400330490000700344520138000351653001801731653002901749653002401778653000801802653000801810100001501818700001601833700003301849700001801882700002101900856012701921       2014                        eng d  a1570-180800aPharmacokinetic modeling of caco-2 cell permeability using genetic programming (GP) method0 aPharmacokinetic modeling of caco2 cell permeability using geneti  aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcNOV  a1112-11180 v113 a<p>An accurate prediction of the pharmacokinetic properties of orally administered drugs is of paramount importance in pharmaceutical industry. Caco-2 cell permeability is a well established parameter for assessing the drug absorption profiles of lead molecules. Due to the restrictions on animal testing, prohibitive in situ models and ethical issues, the development of predictive models is essential. Genetic programming (GP) is an artificial intelligence (AI)-based exclusively data driven modeling paradigm. Given an example input-output data, it searches and optimizes, both the structure and parameters of a well fitting linear/non-linear input-output model. Despite this novelty, GP has not been widely exploited in drug design. Accordingly, in this study we propose a GP based approach for the in silico prediction of Caco-2 cell permeability using a diverse set of molecules. The predictions yielded a high magnitude for the training and test set correlation coefficient with low RMSE, indicating accurate Caco-2 permeability prediction and generalization performance by the GP model. The predictions were better or comparable to artificial neural networks (ANN) and support vector regression (SVR) methods. The GP based modeling approach illustrated will find diverse applications in (QSAR, QSPR and QSTR) modeling for the virtual screening of large libraries.</p>10aADME modeling10aCaco-2 cell permeability10agenetic programming10aMLP10aSVR1 aVyas, Renu1 aGoel, Purva1 aKarthikeyan, Muthukumarasamy1 aTambe, S., S.1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/pharmacokinetic-modeling-caco-2-cell-permeability-using-genetic-programming-gp-method-002420nas a2200169   4500008004100000245015100041210006900192260000800261300001200269490000700281520175000288100001602038700001902054700002402073700002302097856013002120       2017                        eng d00aPrediction of degrees API values of crude oils by use of saturates/aromatics/resins/ asphaltenes analysis: computational-intelligence-based models0 aPrediction of degrees API values of crude oils by use of saturat  cJUN  a817-8530 v223 aThe degrees API value is an important physicochemical characteristic of crude oils often used in determining their properties and quality. There exist models-predominantly linear ones-for predicting the degrees API magnitude from the molecular composition of a crude oil. This approach is tedious and time-consuming because it requires quantitative determination of numerous crude-oil components. Usually, the hydrocarbons present in a crude oil are grouped according to their molecular average structures into saturates, aromatics, resins, and asphaltenes (SARA) fractions. An degrees API-value prediction model dependent on these four fractions is relatively easier to develop, although this approach has been rarely used. A rigorous scrutiny suggests that some of the dependencies between the individual SARA fractions and the corresponding degrees API value could be nonlinear. Accordingly, in this study, SARA-fraction-based nonlinear models have been developed for the prediction of values using three computational-intelligence (CI) formalisms: genetic programming (GP), artificialneural networks (ANNs), and support-vector regression (SVR). The SARA analyses and degrees API values of 403 crude-oil samples covering wide ranges have been used in developing these models. A comparison of the CI-based models with an existing linear model indicates that all the former class of models possess a significantly better degrees API-value prediction and generalization performance than those exhibited by the linear model. Also, the SVR-based model has been found to be the most accurate degrees API-value predictor. Because of their better prediction accuracy, CI-based models can be gainfully used to predict degrees API values of crude oils.1 aGoel, Purva1 aSaurabh, Kumar1 aPatil-Shinde, Veena1 aTambe, Sanjeev, S.  uhttps://library.ncl.res.in/content/prediction-degrees-api-values-crude-oils-use-saturatesaromaticsresins-asphaltenes-analysis