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      Deep learning for molecular generation

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          Image-to-Image Translation with Conditional Adversarial Networks

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            Planning chemical syntheses with deep neural networks and symbolic AI

            To plan the syntheses of small organic molecules, chemists use retrosynthesis, a problem-solving technique in which target molecules are recursively transformed into increasingly simpler precursors. Computer-aided retrosynthesis would be a valuable tool but at present it is slow and provides results of unsatisfactory quality. Here we use Monte Carlo tree search and symbolic artificial intelligence (AI) to discover retrosynthetic routes. We combined Monte Carlo tree search with an expansion policy network that guides the search, and a filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on essentially all reactions ever published in organic chemistry. Our system solves for almost twice as many molecules, thirty times faster than the traditional computer-aided search method, which is based on extracted rules and hand-designed heuristics. In a double-blind AB test, chemists on average considered our computer-generated routes to be equivalent to reported literature routes.
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              Hybrid computing using a neural network with dynamic external memory

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                Author and article information

                Journal
                Future Medicinal Chemistry
                Future Medicinal Chemistry
                Future Science Ltd
                1756-8919
                1756-8927
                March 2019
                March 2019
                : 11
                : 6
                : 567-597
                Affiliations
                [1 ]Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, PR China
                [2 ]BNLMS, State Key Laboratory for Structural Chemistry of Unstable & Stable Species, College of Chemistry & Molecular Engineering, Peking University, Beijing, 100871, PR China
                [3 ]PTN Graduate Program, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, PR China
                Article
                10.4155/fmc-2018-0358
                30698019
                38306a1e-87fe-4196-a87b-e80fb7763525
                © 2019
                History

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