Metabolic pathway synthesis based on predicting compound transformable pairs by using neural classifiers with imbalanced data handling

Automatic in silico synthesis of metabolic pathway can practically reduce the cost of wet laboratories. To achieve this, predicting whether or not two metabolites are transformable is the first essential step. The problems of predicting the possibility of transforming one metabolite into another and how to computationally synthesize a metabolic pathway were studied. These two problems were transformed to the problem of classifying features of metabolite pairs into transformable or non-transformable classes. The following two main issues were contributed in this study: (1) two new feature schemes, i.e. the projected features on their first principal component and the average features, for representing transform-ability of each metabolite pair using 2D and 3D compound structural features and (2) a method of modified imbalanced data handling by adding synthetic boundary data of different classes to balance data. Based on the E. coli reference pathways, the results of proposed features with feature selection and our imbalanced data handling approach show the better performance than the results from other methods when evaluated by several metrics. Our significant feature group possibly achieves high classification correctness of computational pathway synthesis. In pathway recovery results by a group of neural network models, 19 pathways were significantly recovered by our feature group at each recovery ratio of at least 0.5, whereas the other compared feature group gave only four significantly recovered pathways.