AI-assisted synthesis prediction

Johansson, Stina; Thakkar, Amol; Kogej, Thierry; Bjerrum, Esben Jannik; Genheden, Samuel; Bastys, Tomas; Kannas, Christos; Schliep, Alexander; Chen, Hongming; Engkvist, Ola

doi:10.1016/j.ddtec.2020.06.002

Cited by 42 publications

(41 citation statements)

References 39 publications

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“…The integrated de novo design platform can be further expanded by explicitly including the structural diversity of the molecular designs in the multiparameter optimization process. To achieve a broader exploration of the chemical space, several strategies can be adopted, for example, choosing different optimization criteria for the generative deep learning model or including other artificial intelligence models (15,56) in the definition of compatible organic reactions. The proposed approach demonstrates the possibility to achieve closed-loop benchtop platforms for compound design and iterative optimization driven by artificial intelligence.…”

Section: Study Significance and Outlookmentioning

confidence: 99%

Combining generative artificial intelligence and on-chip synthesis for de novo drug design

et al. 2021

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Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space of known LXRα agonists and generate novel molecular candidates. To ensure compatibility with automated on-chip synthesis, the chemical space was confined to the virtual products obtainable from 17 one-step reactions. Twenty-five de novo designs were successfully synthesized in flow. In vitro screening of the crude reaction products revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch resynthesis, purification, and retesting of 14 of these compounds confirmed that 12 of them were potent LXR agonists. These results support the suitability of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.

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Section: Study Significance and Outlookmentioning

confidence: 99%

Combining generative artificial intelligence and on-chip synthesis for de novo drug design

et al. 2021

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“…NLP is increasingly being recognized as a means to meaningfully engage with scientific literature and to extract user‐specific information from a large corpus of texts 31 . Select demonstrations of this approach range from regular expression and syntax‐based identification of structure‐property‐values pairs from literature (battery materials, 45 phase diagrams, 75 inorganic oxides 48 ), variational autoencoder‐driven prediction of synthesis parameters (inorganic oxides), 76 the discovery of new thermoelectrics using word embeddings, 31 and the identification of broad synthesis recipes using neural networks 77‐80 …”

Section: Grand Challenges In Glass Science Engineering and Technologymentioning

confidence: 99%

Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21^st century

Ravinder

Venugopal

Bishnoi

et al. 2021

Int J of Appl Glass Sci

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The progress of human civilization has always been closely associated with the discovery of new materials. This is probably why the tripartite classification of historical periods is also based on materials-stone, bronze, and iron age.Beyond these materials, there are several others which have significantly improved the quality of human life, namely, steel, aluminum, glass, plastics, the latest in the list being nanomaterials. Among these materials, glasses hold a unique place in human lives, considering their applications ranging from everyday glass utensils and kitchen-wares to

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“…Computer-assisted synthetic planning (CASP) comprises a range of artificial intelligence approaches to predict reaction products from reactant or reagents, or vice-versa, and to plan retrosynthesis. [4][5][6][7][8][9][10][11][12] Here we asked the question whether CASP might be exploited to predict the outcome of enzymatic reactions for organic synthesis. Recent efforts in predicting enzymatic reactions focused on metabolic reactions from the KEGG enzymatic reaction database and predictions of drug metabolism, [13][14][15] as well as retrosynthetic planning with enzymatic reactions using a template based approach.…”

Section: Introductionmentioning

confidence: 99%

Predicting enzymatic reactions with a molecular transformer

2021

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AI-assisted synthesis prediction

Cited by 42 publications

References 39 publications

Combining generative artificial intelligence and on-chip synthesis for de novo drug design

Combining generative artificial intelligence and on-chip synthesis for de novo drug design

Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21^st century

Predicting enzymatic reactions with a molecular transformer

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AI-assisted synthesis prediction

Cited by 42 publications

References 39 publications

Combining generative artificial intelligence and on-chip synthesis for de novo drug design

Combining generative artificial intelligence and on-chip synthesis for de novo drug design

Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21st century

Predicting enzymatic reactions with a molecular transformer

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Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21^st century