The functional screening of compounds is an important topic in chemistry and biomedicine that can uncover
the essential properties of compounds and provide information concerning their correct use. In this study, we investigated
the bioactive compounds reported in Selleckchem, which were assigned to 22 pathways. A computational method was
proposed to identify the pathways of the bioactive compounds. Unlike most existing methods that only consider
compound structural information, the proposed method adopted both the structural and interaction information from the
compounds. The total accuracy achieved by our method was 61.79% based on jackknife analysis of a dataset of 1,832
bioactive compounds. Its performance was quite good compared with that of other machine learning algorithms (with total
accuracies less than 46%). Finally, some of the false positives obtained by the method were analyzed to investigate the
likelihood of compounds being annotated to new pathways.
Keywords: Bioactive compound, chemical-chemical interaction, chemical structure similarity, SMILES.
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