Hi all,
Tomorrow (sorry for the late email) we'll hear from Theophile Gaudin from
IBM Research Zurich at group meeting. See below for the abstract for his
talk.
See you there, and best,
Ian
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There is an intuitive analogy of an organic chemist's understanding of a
compound and a language speaker's understanding of a word. Consequently, it
is possible to introduce the basic concepts and analyze potential impacts
of linguistic analysis to the world of organic chemistry. In this work, we
cast the reaction prediction task as a translation problem by introducing a
template-free sequence-to-sequence model, trained end-to-end and fully
data-driven. We propose a novel way of tokenization, which is arbitrarily
extensible with reaction information. With this approach, we demonstrate
results superior to the state-of-the-art solution by a significant margin
on the top-1 accuracy. Specifically, our approach achieves an accuracy of
80.1% without relying on auxiliary knowledge such as reaction templates.
Also, 66.4% accuracy is reached on a larger and noisier dataset.
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