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-002226nas a2200253   4500008004100000022001400041245010200055210006900157260011100226300001200337490000700349520123600356653001901592653001201611653003101623653003801654653001801692653003901710100001501749700003301764700002001797700002701817856012801844       2015                        eng d  a1386-207300aPharmacophore and docking based virtual screening of validated mycobacterium tuberculosis targets0 aPharmacophore and docking based virtual screening of validated m  aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcAUG  a624-6370 v183 a<p>Target based virtual screening has surpassed ligand based virtual screening methods in the recent past mainly as it provides more clues regarding intermolecular interactions and takes into consideration the flexible receptor as well. The current methodology describes a computational strategy of predicting Mycobacterium tuberculosis (M. tuberculosis) binders for five well studied targets representing M. tuberculosis proteome encompassing most of the known mechanisms of action. The diversity of the targets was affirmed by their active site analysis and structural studies. The current approach employed pharmacophore searching, docking and clustering techniques in tandem and was validated by enrichment studies using the available Schrodinger data set consisting of 1000 decoys. The application of this methodology was demonstrated by predicting potential molecular targets for fifty newly synthesized compounds. Cross docking studies on the targets were carried out with 4512 known inhibitors utilizing a high performance computing platform to reveal underlying affinity and promiscuity patterns. Optimum binding energy range for all targets as determined by high throughput docking was found to be -3 to -13 kcal/mol.</p>10aBinding energy10adocking10aMycobacterium tuberculosis10aopen source drug discovery (OSDD)10apharmacophore10astructure based drug design (SBDD)1 aVyas, Renu1 aKarthikeyan, Muthukumarasamy1 aNainaru, Ganesh1 aMuthukrishnan, Murugan  uhttps://library.ncl.res.in/content/pharmacophore-and-docking-based-virtual-screening-validated-mycobacterium-tuberculosis-001949nas a2200229   4500008004100000022001400041245007300055210006900128260011100197300001200308490000700320520110300327653001901430653001701449653000801466653002501474653002201499100003301521700004301554700001501597856010701612       2015                        eng d  a1386-207300aPrediction of bioactive compounds using computed NMR chemical shifts0 aPrediction of bioactive compounds using computed NMR chemical sh  aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN  a562-5760 v183 a<p>NMR based chemical shifts are an important diagnostic parameter for structure elucidation as they capture rich information related to conformational, electronic and stereochemical arrangement of functional groups in a molecule which is responsible for its activity towards any biological target. The present work discusses the importance of computing NMR chemical shifts from molecular structures. The NMR chemical shift data (experimental or computed) was used to generate fingerprints in binary formats for mapping molecular fragments (as descriptors) and correlating with the bioactivity classes. For this study, chemical shift data derived binary fingerprints were computed for 149 classes and 4800 bioactive molecules. The sensitivity and selectivity of fingerprints in discriminating molecules belonging to different therapeutic categories was assessed using a LibSVM based classifier. An accuracy of 82% for proton and 94% for carbon NMR fingerprints were obtained for anti-psoriatic and anti-psychotic molecules demonstrating the effectiveness of this approach for virtual screening.</p>10aChemical shift10afingerprints10aNMR10asimilarity searching10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aRajamohanan, Pattuparambil, Ramanpilla1 aVyas, Renu  uhttps://library.ncl.res.in/content/prediction-bioactive-compounds-using-computed-nmr-chemical-shifts-002219nas a2200253   4500008004100000022001400041245006500055210006500120260011100185300001200296490000700308520137700315653001401692653001101706653001201717653001401729653001401743653002101757653002201778100003301800700001901833700001501852856009801867       2015                        eng d  a1386-207300aProtein ligand complex guided approach for virtual screening0 aProtein ligand complex guided approach for virtual screening  aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN  a577-5900 v183 a<p>The target ligand association data is a rich source of information which is not exploited enough for drug design efforts in virtual screening. A java based open-source toolkit for Protein Ligand Network Extraction (J-ProLiNE) focused on protein-ligand complex analysis with several features integrated in a distributed computing network has been developed. Sequence alignment and similarity search components have been automated to yield local, global alignment scores along with similarity and distance scores. 10000 proteins with co-crystallized ligands from pdb and MOAD databases were extracted and analyzed for revealing relationships between targets, ligands and scaffolds. Through this analysis, we could generate a protein ligand network to identify the promiscuous and selective scaffolds for multiple classes of proteins targets. Using J-ProLiNE we created a 507 x 507 matrix of protein targets and native ligands belonging to six enzyme classes and analyzed the results to elucidate the protein-protein, protein-ligand and ligand-ligand interactions. In yet another application of the J-ProLiNE software, we were able to process kinase related information stored in US patents to construct disease-gene-ligand-scaffold networks. It is hoped that the studies presented here will enable target ligand knowledge based virtual screening for inhibitor design.</p>10aComplexes10aligand10aprotein10ascaffolds10asequences10asimilarity score10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aPandit, Deepak1 aVyas, Renu  uhttps://library.ncl.res.in/content/protein-ligand-complex-guided-approach-virtual-screening-002412nas a2200289   4500008004100000022001400041245015200055210006900207260007900276300001200355490000700367520132100374653002401695653003401719653002401753653002101777653002401798100001801822700002401840700001701864700002701881700001501908700002301923700001901946700002601965856013101991       2016                        eng d  a2194-574800aPrediction of reactivity ratios in free radical copolymerization from monomer resonance-polarity (Q-e) parameters: genetic programming-based models0 aPrediction of reactivity ratios in free radical copolymerization  aGENTHINER STRASSE 13, D-10785 BERLIN, GERMANYbWALTER DE GRUYTER GMBHcFEB  a361-3720 v143 a<p>The principal deficiency of the widely utilized Alfrey-Price (AP) scheme for computing reactivity ratios in the widely used free radical copolymerization is that it ignores important factors, such as the steric effects. This often leads to inaccurate reactivity ratio predictions by AP model. Accordingly, in this study, exclusively data-driven, Q-e parameter-based new models have been developed for the reactivity ratio prediction in free radical copolymerization. In the model development, a novel artificial intelligence formalism known as ``genetic programming (GP)'' that performs symbolic regression has been employed. The GP-based models possess a different functional form than AP model. Further, parameters of GP-based models were fine-tuned using Levenberg-Marquardt (LM) nonlinear regression method. A comparison of AP, GP and GP-LM as well as artificial neural network (ANN)-based models indicates that GP and GP-LM models exhibit superior reactivity ratio prediction accuracy and generalization performance (with correlation coefficient magnitudes close to or greater than 0.9) when compared with AP and ANN models. The GPbased reactivity ratio prediction models developed here due to their higher accuracy and generalization capability have the potential of replacing the widely used AP models.</p>10aAlfrey-Price scheme10afree radical copolymerization10agenetic programming10areactivity ratio10asymbolic regression1 aShrinivas, K.1 aKulkarni, Rahul, P.1 aShaikh, Saif1 aGhorpade, Ravindra, V.1 aVyas, Renu1 aTambe, Sanjeev, S.1 aPonrathnam, S.1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/prediction-reactivity-ratios-free-radical-copolymerization-monomer-resonance-polarity-q-e-1