Identification of Chemical-Disease Associations Through Integration of Molecular Fingerprint, Gene Ontology and Pathway Information.

The identification of chemical-disease association types is helpful not only to discovery lead compounds and study drug repositioning, but also to treat disease and decipher pathomechanism. It is very urgent to develop computational method for identifying potential chemical-disease association types, since wet methods are usually expensive, laborious and time-consuming. In this study, molecular fingerprint, gene ontology and pathway are utilized to characterize chemicals and diseases. A novel predictor is proposed to recognize potential chemical-disease associations at the first layer, and further distinguish whether their relationships belong to biomarker or therapeutic relations at the second layer. The prediction performance of current method is assessed using the benchmark dataset based on ten-fold cross-validation. The practical prediction accuracies of the first layer and the second layer are 78.47% and 72.07%, respectively. The recognition ability for lead compounds, new drug indications, potential and true chemical-disease association pairs has also been investigated and confirmed by constructing a variety of datasets and performing a series of experiments. It is anticipated that the current method can be considered as a powerful high-throughput virtual screening tool for drug researches and developments.