Templates are ranked by their SP scores and the ligands corresponding to the to

Alternatively, it has been previously suggested that the genome can be partitioned into disease genes and non disease genes. While such a Boolean distribution is likely INNO-406 SRC 阻害剤 to be overly simplistic, a spectrum of levels of disease association with specific gene subsets might explain this disparity. To drill a little further into the data, we assessed the therapeutic potential of each phenotype using currently available repositioned drugs. We calculated an empirical Targetability Index, defined here as the ratio of the number of predicted targets to the number of predicted candidate genes for each phenotype. The distri bution was bimodal with four phenotypes being more targetable than the other three. A factor which is likely to influence the targetability is our underly ing knowledge of the phenotype.<br><br> If the molecular path ways involved have been previously characterized, there is more likely to be drug target information in the existing drug databases, even if the phenotype has not previously been associated with the molecular Lapatinib 388082-77-7 system. The low TIs for BD and the diabetes phenotypes likely arises from lack of knowledge of underlying path ways. More basic research in this area is required. All three drug databases made significant contributions to target identification, with the highest contribution from DrugBank, followed by TTD and PharmGKB. DrugBank is a chemical as well as a clinical drug database which contains broader coverage of drug targets and broader depth of information compared to the chemi cal drug database TTD and the clinical drug database PharmGKB.<br><br> PharmGKB, being a clinical drug database, has a lower coverage of drug target associations, but broader depth of information compared to TTD. To sum marize, the total coverage of the predicted targets from all three databases was estimated supplier Lonafarnib to be 30% of the candidate genes predicted by Gentrepid, with the maximum contri bution from DrugBank. Discovery of novel therapeutic targets For the seven diseases considered in our study, we per formed a binary classification of the 452 targets to dis tinguish therapeutic targets which were rediscovered from novel potential therapeutic targets. Novel genes are targeted by therapeutics registered for other uses but not for the phenotype of interest. We found 428 novel therapeutic targets accounting for almost 94% of the targets identified in the previous sec tion.<br><br> The remaining 24 targets have therapeutics which either are approved, are in ongoing clinical trials, or have been discontinued as therapeutics for the phenotype of interest. We considered these 24 known tar gets as true positives for the phenotypes of interest in one of the benchmarks described below. Figure 6 shows the number of novel therapeutic targets obtained for each of the seven diseases, along with the contribution from each drug database. The novelty of the predicted targets for each disease was assessed by calcu lating the ratio of the number of novel therapeutic targets to the number of therapeutic targets predicted for each disease. The novelty ratio for all diseases was between 0.