02880nas a2200325   4500008004100000022001400041245011500055210006900170260011100239300001200350490000700362520173200369653001702101653001902118653001902137653002002156653003402176653001602210653001502226653002102241653001802262653001602280100001502296700001802311700001502329700002302344700003302367700002602400856012802426       2015                        eng d  a1386-207300aStudy of applications of machine learning based classification methods for virtual screening of lead molecules0 aStudy of applications of machine learning based classification m  aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcAUG  a658-6720 v183 a<p>The ligand-based virtual screening of combinatorial libraries employs a number of statistical modeling and machine learning methods. A comprehensive analysis of the application of these methods for the diversity oriented virtual screening of biological targets/drug classes is presented here. A number of classification models have been built using three types of inputs namely structure based descriptors, molecular fingerprints and therapeutic category for performing virtual screening. The activity and affinity descriptors of a set of inhibitors of four target classes DHFR, COX, LOX and NMDA have been utilized to train a total of six classifiers viz. Artificial Neural Network (ANN), k nearest neighbor (k-NN), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree - (DT) and Random Forest - (RF). Among these classifiers, the ANN was found as the best classifier with an AUC of 0.9 irrespective of the target. New molecular fingerprints based on pharmacophore, toxicophore and chemophore (PTC), were used to build the ANN models for each dataset. A good accuracy of 87.27% was obtained using 296 chemophoric binary fingerprints for the COX-LOX inhibitors compared to pharmacophoric (67.82 %) and toxicophoric (70.64 %). The methodology was validated on the classical Ames mutagenecity dataset of 4337 molecules. To evaluate it further, selectivity and promiscuity of molecules from five drug classes viz. anti-anginal, anti-convulsant, anti-depressant, anti-arrhythmic and anti-diabetic were studied. The TPC fingerprints computed for each category were able to capture the drug-class specific features using the k-NN classifier. These models can be useful for selecting optimal molecules for drug design.</p>10aAnti-anginal10aanti-arrythmic10aanti-bacterial10aanti-convulsant10aanti-depressant anti-diabetic10abinary QSAR10achemophore10amachine learning10apharmacophore10atoxicophore1 aVyas, Renu1 aBapat, Sanket1 aJain, Esha1 aTambe, Sanjeev, S.1 aKarthikeyan, Muthukumarasamy1 aKulkarni, Bhaskar, D.  uhttp://library.ncl.res.in/content/study-applications-machine-learning-based-classification-methods-virtual-screening-lead-002660nas a2200241   4500008004100000022001400041245013400055210006900189260000800258300001100266490000700277520181500284653001902099653001302118653001502131653002302146653001802169100002902187700002102216700001902237700003402256856012802290       2022                        eng d  a1773-224700aBioinspired hyaluronic acid based nanofibers immobilized with 3, 4-difluorobenzylidene curcumin for treating bacterial infections0 aBioinspired hyaluronic acid based nanofibers immobilized with 3   cAUG  a1034800 v743 a<p>
	Curcumin (Cur) is a natural polyphenol with multifaceted pharmacological functions, exploited extensively for biomedical applications. Traditionally curcumin is being used as an antimicrobial agent. However, to improvise the pharmacological properties, it is being modified synthetically. One of such modified Cur is 3, 4- difluorobenzylidene curcumin (CDF) which is aimed for enhancing the anti-cancer properties. Though there are reports on the studies of anti-cancer properties involving CDF, the anti-bacterial property is yet to be demonstrated. Accordingly, in our studies, we prepared bioinspired hyaluronic acid blends immobilized with CDF and fabricated non-woven nanofiber mats. These nanofiber mats were characterized and demonstrated in vitro cell culture studies, which involved cell viability, hemolysis, anti-bacterial and cell scratch assay to understand their efficacy in treating bacteria. The molecular docking studies of CDF and Cur were performed on the dihydrofolate reductase (DHFR) enzyme receptor, which is an essential protein of S.auerus (Staphylococcus aureus). The results of MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) assay, and hemolysis of the respective nanofiber mats with Cur and CDF showed non-toxicity and were compatible with blood cells. Further, the cell proliferation and adherence recorded &gt;60% fibroblast cells for the nanofiber mats. The anti-bacterial property of Cur and CDF was similar. The in vitro release studies for the respective Cur and CDF loaded nanofiber mats recorded a release of 25 and 37%, respectively. From these studies, we concluded that the CDF sustained its antibacterial property in addition to the improved anti-cancer property; hence CDF being synergetic, it will have a better scope in cancer therapy.</p>
10aanti-bacterial10acurcumin10ananofibers10atissue engineering10awound Healing1 aPatel, Pratikshkumar, R.1 aSingam, Amarnath1 aIyer, Arun, K.1 aGundloori, Rathna, Venkata Na  uhttp://library.ncl.res.in/content/bioinspired-hyaluronic-acid-based-nanofibers-immobilized-3-4-difluorobenzylidene-curcumin