Predicting the types of metabolic pathway of compounds using molecular fragments and sequential minimal optimization.

In this study, compounds are first represented by molecular fragments which are then delivered to a prediction engine called Sequential Minimal Optimization (SMO) for predictions. Maximum relevance and minimum redundancy (mRMR) and incremental feature selection are adopted to extract key features based on which an optimal prediction engine is established. The proposed method is effective comparing to the random forest, Dagging and a popular method that integrating chemical-chemical interactions and chemical-chemical similarities. We also make predictions using some compounds with unknown metabolic pathways and choose 17 compounds for analysis. The results indicate that the method proposed may become a useful tool in predicting and analyzing metabolic pathways.
PMID: 26552441 [PubMed - as supplied by publisher]

Source: Combinatorial Chemistry and High Throughput Screening - November 10, 2015 Category: Chemistry Authors: Chen L, Chu C, Feng K Tags: Comb Chem High Throughput Screen Source Type: research