51,420 research outputs found
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
Representation and use of chemistry in the global electronic age.
We present an overview of the current state of public semantic chemistry and propose new approaches at a strategic and a detailed level. We show by example how a model for a Chemical Semantic Web can be constructed using machine-processed data and information from journal articles.This manuscript addresses questions of robotic access to data and its automatic re-use, including the role of Open Access archival of data. This is a pre-refereed preprint allowed by the publisher's (Royal Soc. Chemistry) Green policy. The author's preferred manuscript is an HTML hyperdocument with ca. 20 links to images, some of which are JPEgs and some of which are SVG (scalable vector graphics) including animations. There are also links to molecules in CML, for which the Jmol viewer is recommended. We susgeest that readers who wish to see the full glory of the manuscript, download the Zipped version and unpack on their machine. We also supply a PDF and DOC (Word) version which obviously cannot show the animations, but which may be the best palce to start, particularly for those more interested in the text
A case study in model-driven synthetic biology
We report on a case study in synthetic biology, demonstrating the modeldriven
design of a self-powering electrochemical biosensor. An essential result of
the design process is a general template of a biosensor, which can be instantiated
to be adapted to specific pollutants. This template represents a gene expression network
extended by metabolic activity. We illustrate the model-based analysis of this
template using qualitative, stochastic and continuous Petri nets and related analysis
techniques, contributing to a reliable and robust design
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