id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-252166-qah877pk	Ekins, S	In silico pharmacology for drug discovery: applications to targets and beyond	2007-09-01		.txt	text/plain	12663	571	41	These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. This included the development of methods and databases, quantitative structure-activity relationships (QSARs), similarity searching, pharmacophores, homology models and other molecular modelling, machine learning, data mining, network analysis and data analysis tools that all use a computer. For example, one study used probabilistic neural networks with 24 atom-type descriptors to classify 799 molecules from the MDL Drug Data Reports (MDDR) database with activity against one of the seven targets (G protein-coupled receptors (GPCRs), kinases, enzymes, nuclear hormone receptors and zinc peptidases) with excellent training, testing and prediction statistics (Niwa, 2004) . In silico pharmacology for drug discovery S Ekins et al derived with 40 molecules with activities over three log orders to result in a five-feature pharmacophore model.	./cache/cord-252166-qah877pk.txt	./txt/cord-252166-qah877pk.txt