Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/80455
Título: | Predicting the number of biochemical transformations needed to synthesize a compound |
Autor(es): | Correia, João Carreira, Rafael Pereira, Vítor Rocha, Miguel |
Palavras-chave: | biochemical transformations biosynthesis deep learning reaction rules |
Data: | 18-Jul-2022 |
Editora: | IEEE |
Revista: | IEEE International Joint Conference on Neural Networks (IJCNN) |
Citação: | Correia, João; Carreira, Rafael; Pereira, Vítor; Rocha, Miguel, Predicting the number of biochemical transformations needed to synthesize a compound. IJCNN 2022 - International Joint Conference on Neural Networks. Padua, Italy, July 18-23, 1-8, 2022. |
Resumo(s): | Exploiting the natural metabolic abilities of microorganisms for the production of bioactive compounds has been a research problem of great interest. The economical and environmental costs associated with petrochemical-derived industries have promoted the emergence of biochemical processes from renewable carbon sources. However, optimally rewiring microbial metabolism in a competitive and sustainable manner is still a challenge. Recently, some retrobiosynthesis tools for the design of de novo biosynthetic pathways have been proposed. These tools generate a large number of intermediate compounds that are beyond experimental feasibility. Thus, effective methods to reduce the number of compounds by selecting the most promising ones are still needed. Here, we propose the use of classification and regression deep learning models, such as fully-connected neural networks and 1D convolutional neural networks, to predict the number of biochemical transformations needed to produce a compound. The data to train and evaluate the models was generated using a set of 13055 reaction rules and 673 compounds from Escherichia coli metabolism as starting compounds. The data was generated up to 5 steps resulting in a dataset of over 2.6 million compounds. This approach can be effectively used in biochemical applications, including retrobiosyntesis, to prioritize compounds that can be produced using fewer biochemical transformations. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/80455 |
ISBN: | 9781728186719 |
DOI: | 10.1109/IJCNN55064.2022.9892124 |
ISSN: | 2161-4393 |
Versão da editora: | https://ieeexplore.ieee.org/xpl/conhome/9891857/proceeding |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CEB - Artigos em Livros de Atas / Papers in Proceedings |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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document_55864_1.pdf | 204,22 kB | Adobe PDF | Ver/Abrir |