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- Johansson, Simon, 1994, et al.
(author)
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AI-assisted synthesis prediction
- 2019
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In: Drug Discovery Today: Technologies. - : Elsevier BV. - 1740-6749. ; 32-33:December, s. 65-72
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Journal article (peer-reviewed)abstract
- Application of AI technologies in synthesis prediction has developed very rapidly in recent years. We attempt here to give a comprehensive summary on the latest advancement on retro-synthesis planning, forward synthesis prediction as well as quantum chemistry-based reaction prediction models. Besides an introduction on the AI/ML models for addressing various synthesis related problems, the sources of the reaction datasets used in model building is also covered. In addition to the predictive models, the robotics based high throughput experimentation technology will be another crucial factor for conducting synthesis in an automated fashion. Some state-of-the-art of high throughput experimentation practices carried out in the pharmaceutical industry are highlighted in this chapter to give the reader a sense of how future chemistry will be conducted to make compounds faster and cheaper. © 2020 Elsevier Ltd
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| 2. |
- Shevtsov, Oleksii, 1988, et al.
(author)
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A de novo molecular generation method using latent vector based generative adversarial network
- 2019
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In: Journal of Cheminformatics. - : Springer Science and Business Media LLC. - 1758-2946 .- 1758-2946. ; 11:1
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Journal article (peer-reviewed)abstract
- Deep learning methods applied to drug discovery have been used to generate novel structures. In this study, we propose a new deep learning architecture, LatentGAN, which combines an autoencoder and a generative adversarial neural network for de novo molecular design. We applied the method in two scenarios: One to generate random drug-like compounds and another to generate target-biased compounds. Our results show that the method works well in both cases. Sampled compounds from the trained model can largely occupy the same chemical space as the training set and also generate a substantial fraction of novel compounds. Moreover, the drug-likeness score of compounds sampled from LatentGAN is also similar to that of the training set. Lastly, generated compounds differ from those obtained with a Recurrent Neural Network-based generative model approach, indicating that both methods can be used complementarily.[Figure not available: See fulltext.]
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