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Further, ROC curves were created in GraphPad Prism 6 software considering INNO-406 bcr-Abl 阻害剤 four thresholds of at least one, five, ten and fifteen citations. Non linear regression analysis was also performed to fit the ROC curves. For more details about the content of these databases see Methods. The total number of unique targets from all the data bases was 2,494 genes, which is 8% of the entire human genome. Previously, it was estimated that 3,000 5,000 genes are druggable which is 10 17% of the entire genome. The gap between extracted targets from the three drug databases and the estimated number of druggable genes exists because many drug gable genes have not yet been mapped to a phenotype and thus there has been no imperative to develop drugs for these targets.<br><br> The targets searched in our study cover 50 83% of the possible druggable genes mentioned in previous studies. We compared raw data such as drugs and drug targets across the three drug databases to determine the redun dancy of the information in these databases. With respect to drug targets, Lapatinib EGFR 阻害剤 only 4% of human drug target entries were common to all three databases. When the data bases were compared in a pairwise fashion, the proportion of common targets ranged from 9 18%. Each of the data bases contains a significant amount of information that is unique to that database. TTD has the fewest unique tar gets, while DrugBank and PharmGKB have 1,495 and 326 unique targets respectively. We also compared the number of drugs present in three drug databases.<br><br> Of the combined total of 9,991 unique drugs, DrugBank contributes 50% オーダー Lonafarnib of the unique drug compounds, while TTD and PharmGKB, contribute 18% and 15% of the unique drug compounds respectively. Using pairwise comparisons to check redundancy of drugs between the databases, we observed TTD and PharmGKB share 15 19% of their listed drugs with DrugBank. Although there is significant overlap among the three databases, the high number of unique drugs in each database show the databases are fairly complementary. In summary, all three drug data bases contain unique and valuable data and were thus all used in the subsequent analysis. Identification of therapeutic targets We identified potential therapeutic targets for the seven complex diseases from the Gentrepid predicted candi date genes generated by our reanalysis of the WTCCC data.<br><br> In total, Gentrepid predicted 1,497 candidate genes for all seven diseases, comprising by phenotype, Type 2 Diabetes, Bipolar Disorder, Crohns Disease, Hypertension, Type 1 Diabetes, Coronary Artery Disease and Rheumatoid Arthritis. We searched for these candidate genes in the drug gene target files obtained from all three drug databases and found 452 potential therapeutic targets for the seven complex dis eases. This illustrates that almost 30% of the total number of predicted candidate genes by Gentrepid are potential targets for therapeutic treatments using currently available drugs. The disparity between the 8% of the human genome that is targettable and the 30% of pre dicted candidate genes that are targettable is interesting and should be investigated further. The enrichment of druggable tar gets in the candidate gene set might be a selection effect, either at the SNP level, or at the knowledgebase level, it might suggest that we already know more about disease genes than the genome in general.