32,642 research outputs found
ChemTS: An Efficient Python Library for de novo Molecular Generation
Automatic design of organic materials requires black-box optimization in a
vast chemical space. In conventional molecular design algorithms, a molecule is
built as a combination of predetermined fragments. Recently, deep neural
network models such as variational auto encoders (VAEs) and recurrent neural
networks (RNNs) are shown to be effective in de novo design of molecules
without any predetermined fragments. This paper presents a novel python library
ChemTS that explores the chemical space by combining Monte Carlo tree search
(MCTS) and an RNN. In a benchmarking problem of optimizing the octanol-water
partition coefficient and synthesizability, our algorithm showed superior
efficiency in finding high-scoring molecules. ChemTS is available at
https://github.com/tsudalab/ChemTS
Retrosynthetic reaction prediction using neural sequence-to-sequence models
We describe a fully data driven model that learns to perform a retrosynthetic
reaction prediction task, which is treated as a sequence-to-sequence mapping
problem. The end-to-end trained model has an encoder-decoder architecture that
consists of two recurrent neural networks, which has previously shown great
success in solving other sequence-to-sequence prediction tasks such as machine
translation. The model is trained on 50,000 experimental reaction examples from
the United States patent literature, which span 10 broad reaction types that
are commonly used by medicinal chemists. We find that our model performs
comparably with a rule-based expert system baseline model, and also overcomes
certain limitations associated with rule-based expert systems and with any
machine learning approach that contains a rule-based expert system component.
Our model provides an important first step towards solving the challenging
problem of computational retrosynthetic analysis
Designing algorithms to aid discovery by chemical robots
Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery
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