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      Molecular de-novo design through deep reinforcement learning

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          Abstract

          This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target. As a proof of principle, the model is first trained to generate molecules that do not contain sulphur. As a second example, the model is trained to generate analogues to the drug Celecoxib, a technique that could be used for scaffold hopping or library expansion starting from a single molecule. Finally, when tuning the model towards generating compounds predicted to be active against the dopamine receptor type 2, the model generates structures of which more than 95% are predicted to be active, including experimentally confirmed actives that have not been included in either the generative model nor the activity prediction model.

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          The online version of this article (doi:10.1186/s13321-017-0235-x) contains supplementary material, which is available to authorized users.

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          Most cited references 29

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          Long Short-Term Memory

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            Mastering the game of Go with deep neural networks and tree search.

            The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses 'value networks' to evaluate board positions and 'policy networks' to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.
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              ChEMBL: a large-scale bioactivity database for drug discovery

              ChEMBL is an Open Data database containing binding, functional and ADMET information for a large number of drug-like bioactive compounds. These data are manually abstracted from the primary published literature on a regular basis, then further curated and standardized to maximize their quality and utility across a wide range of chemical biology and drug-discovery research problems. Currently, the database contains 5.4 million bioactivity measurements for more than 1 million compounds and 5200 protein targets. Access is available through a web-based interface, data downloads and web services at: https://www.ebi.ac.uk/chembldb.
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                Author and article information

                Contributors
                m.olivecrona@gmail.com
                thomas.blaschke@astrazeneca.com
                ola.engkvist@astrazeneca.com
                hongming.chen@astrazeneca.com
                Journal
                J Cheminform
                J Cheminform
                Journal of Cheminformatics
                Springer International Publishing (Cham )
                1758-2946
                4 September 2017
                4 September 2017
                2017
                : 9
                Affiliations
                ISNI 0000 0001 1519 6403, GRID grid.418151.8, Hit Discovery, Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, , AstraZeneca R&D Gothenburg, ; 43183 Mölndal, Sweden
                Article
                235
                10.1186/s13321-017-0235-x
                5583141
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100010665, H2020 Marie Skłodowska-Curie Actions;
                Award ID: 676434
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Chemoinformatics

                reinforcement learning, recurrent neural networks, de novo design

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