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Table 8 Drug target distribution in different pathway databases

From: Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction

Database #drug target in database #Non-drug target in database % of proteins in database that are drug targets % of all drug targets with pathway info % of all non-drug targets with pathway info
UniProt (all prots) 1953 113741 1.69 83.97 8.33
PID 394 1261 23.81 20.17 1.11
Reactome 1262 4215 23.04 64.62 3.71
KEGG 1414 7473 15.91 72.40 6.57
  1. 84% of all drug-targets under study have pathway information. KEGG includes the highest number of drug targets (72%), followed by Reactome (65%). The number of non-drug targets in each database is small compared to all non-drug targets in UniProt, suggesting that pathways might be enriched for drug targets. Only 1.69% of UniProt proteins are drug targets while they constitute 15.9%-23.8% of pathway databases. This observation is confirmed by comparing the percentages of drug targets and non-drug targets in UniProt to those per database: KEGG, for example, contains 72.4% of all drug targets but only 6.6% of all non-drug targets found in UniProt.