Discovery of novel chemical reactions by deep generative recurrent neural network
Abstract The “creativity” of Artificial Intelligence (AI) in terms of generating de novo molecular structures opened a novel paradigm in compound design, weaknesses (stability & feasibility issues of such structures) notwithstanding. Here we show that “creative” AI may be as successfully taught...
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Nature Portfolio
2021
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oai:doaj.org-article:3d91d19a63b3499ebdae5082b570347c2021-12-02T14:06:24ZDiscovery of novel chemical reactions by deep generative recurrent neural network10.1038/s41598-021-81889-y2045-2322https://doaj.org/article/3d91d19a63b3499ebdae5082b570347c2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81889-yhttps://doaj.org/toc/2045-2322Abstract The “creativity” of Artificial Intelligence (AI) in terms of generating de novo molecular structures opened a novel paradigm in compound design, weaknesses (stability & feasibility issues of such structures) notwithstanding. Here we show that “creative” AI may be as successfully taught to enumerate novel chemical reactions that are stoichiometrically coherent. Furthermore, when coupled to reaction space cartography, de novo reaction design may be focused on the desired reaction class. A sequence-to-sequence autoencoder with bidirectional Long Short-Term Memory layers was trained on on-purpose developed “SMILES/CGR” strings, encoding reactions of the USPTO database. The autoencoder latent space was visualized on a generative topographic map. Novel latent space points were sampled around a map area populated by Suzuki reactions and decoded to corresponding reactions. These can be critically analyzed by the expert, cleaned of irrelevant functional groups and eventually experimentally attempted, herewith enlarging the synthetic purpose of popular synthetic pathways.William BortIgor I. BaskinTimur GimadievArtem MukanovRamil NugmanovPavel SidorovGilles MarcouDragos HorvathOlga KlimchukTimur MadzhidovAlexandre VarnekNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021) |
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Medicine R Science Q |
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Medicine R Science Q William Bort Igor I. Baskin Timur Gimadiev Artem Mukanov Ramil Nugmanov Pavel Sidorov Gilles Marcou Dragos Horvath Olga Klimchuk Timur Madzhidov Alexandre Varnek Discovery of novel chemical reactions by deep generative recurrent neural network |
| description |
Abstract The “creativity” of Artificial Intelligence (AI) in terms of generating de novo molecular structures opened a novel paradigm in compound design, weaknesses (stability & feasibility issues of such structures) notwithstanding. Here we show that “creative” AI may be as successfully taught to enumerate novel chemical reactions that are stoichiometrically coherent. Furthermore, when coupled to reaction space cartography, de novo reaction design may be focused on the desired reaction class. A sequence-to-sequence autoencoder with bidirectional Long Short-Term Memory layers was trained on on-purpose developed “SMILES/CGR” strings, encoding reactions of the USPTO database. The autoencoder latent space was visualized on a generative topographic map. Novel latent space points were sampled around a map area populated by Suzuki reactions and decoded to corresponding reactions. These can be critically analyzed by the expert, cleaned of irrelevant functional groups and eventually experimentally attempted, herewith enlarging the synthetic purpose of popular synthetic pathways. |
| format |
article |
| author |
William Bort Igor I. Baskin Timur Gimadiev Artem Mukanov Ramil Nugmanov Pavel Sidorov Gilles Marcou Dragos Horvath Olga Klimchuk Timur Madzhidov Alexandre Varnek |
| author_facet |
William Bort Igor I. Baskin Timur Gimadiev Artem Mukanov Ramil Nugmanov Pavel Sidorov Gilles Marcou Dragos Horvath Olga Klimchuk Timur Madzhidov Alexandre Varnek |
| author_sort |
William Bort |
| title |
Discovery of novel chemical reactions by deep generative recurrent neural network |
| title_short |
Discovery of novel chemical reactions by deep generative recurrent neural network |
| title_full |
Discovery of novel chemical reactions by deep generative recurrent neural network |
| title_fullStr |
Discovery of novel chemical reactions by deep generative recurrent neural network |
| title_full_unstemmed |
Discovery of novel chemical reactions by deep generative recurrent neural network |
| title_sort |
discovery of novel chemical reactions by deep generative recurrent neural network |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/3d91d19a63b3499ebdae5082b570347c |
| work_keys_str_mv |
AT williambort discoveryofnovelchemicalreactionsbydeepgenerativerecurrentneuralnetwork AT igoribaskin discoveryofnovelchemicalreactionsbydeepgenerativerecurrentneuralnetwork AT timurgimadiev discoveryofnovelchemicalreactionsbydeepgenerativerecurrentneuralnetwork AT artemmukanov discoveryofnovelchemicalreactionsbydeepgenerativerecurrentneuralnetwork AT ramilnugmanov discoveryofnovelchemicalreactionsbydeepgenerativerecurrentneuralnetwork AT pavelsidorov discoveryofnovelchemicalreactionsbydeepgenerativerecurrentneuralnetwork AT gillesmarcou discoveryofnovelchemicalreactionsbydeepgenerativerecurrentneuralnetwork AT dragoshorvath discoveryofnovelchemicalreactionsbydeepgenerativerecurrentneuralnetwork AT olgaklimchuk discoveryofnovelchemicalreactionsbydeepgenerativerecurrentneuralnetwork AT timurmadzhidov discoveryofnovelchemicalreactionsbydeepgenerativerecurrentneuralnetwork AT alexandrevarnek discoveryofnovelchemicalreactionsbydeepgenerativerecurrentneuralnetwork |
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1718392014971600896 |