The first step to predict the outcome of a chemical reaction is to classify existing chemical reactions, on the basis of which possible outcome of unknown reaction can be predicted. There are two approaches for classification of chemical reactions: Model-Driven and Data-Driven. In model-driven approach, chemical structures are usually stored in a computer as molecular graphs. Such graphs can also be represented as matrices. The most preferred matrix representation to store molecular graph is Bond-Electron matrix (BE-matrix). The Reaction matrix (R-matrix) of a chemical reaction can be obtained from the BE-matrices of educts and products was shown by Ugi and his co-workers. Ugi's Scheme comprises of 30 reaction classes according to which reactions can be classified, but in spite of such reaction classes there were several reactions which could not be classified. About 4000 reactions were studied in this work from The Chemical Thesaurus (a chemical reaction database) and accordingly 24 new classes have emerged which led to the extension of Ugi's Scheme. An efficient algorithm based on the extended Ugi's scheme have been developed for classification of chemical reactions. Reaction matrices being symmetric, matrix implementation of extended Ugi's scheme using conventional upper/lower tri-angular matrix is of O(n2) in terms of space complexity. Time complexity of similar matrix implementation is O(n2) in worst case. The authors' proposed algorithm uses two fixed size look-up tables in a novel way and requires constant space complexity. Worst case time complexity of their algorithm although still O(n2) but it outperforms conventional matrix implementation when number of atoms or components in the chemical reaction is 4 or more.
There are two approaches for classification of chemical reactions: Model-Driven and Data-Driven. In this paper, the authors develop an efficient algorithm based on a model-driven approach developed by Ugi and co-workers for classification of chemical reactions. The authors’ algorithm takes reaction matrix of a chemical reaction as input and generates its appropriate class as output. Reaction matrices being symmetric, matrix implementation of Ugi’s scheme using upper/lower tri-angular matrix is of O(n2) in terms of space complexity. Time complexity of similar matrix implementation is O(n4), both in worst case as well as in average case. The proposed algorithm uses two fixed size look-up tables in a novel way and requires constant space complexity. Time complexity both in worst and average cases of the algorithm is linear.
There are two approaches for classification of chemical reactions: Model-Driven and Data-Driven. In this paper, the authors develop an efficient algorithm based on a model-driven approach developed by Ugi and co-workers for classification of chemical reactions. The authors’ algorithm takes reaction matrix of a chemical reaction as input and generates its appropriate class as output. Reaction matrices being symmetric, matrix implementation of Ugi’s scheme using upper/lower tri-angular matrix is of O(n2) in terms of space complexity. Time complexity of similar matrix implementation is O(n4), both in worst case as well as in average case. The proposed algorithm uses two fixed size look-up tables in a novel way and requires constant space complexity. Time complexity both in worst and average cases of the algorithm is linear.
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