Macs in Chemistry

Insanely Great Science

artificial intelligence

RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry

 

The first announcement of a meeting to be held next year.

RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry Friday, 15th June 2018 Royal Society of Chemistry at Burlington House, London, UK.
Twitter hashtag - #RSC_AIChem

AIfirst_announcement

Artificial Intelligence is presently experiencing a renaissance in development of new methods and practical applications to ongoing challenges in Chemistry. We are pleased to announce that the Biological & Medicinal Chemistry Sector (BMCS) and Chemical Information & Computer Applications Group (CICAG) of the Royal Society of Chemistry are organising a one-day conference entitled Artificial Intelligence in Chemistry to present the current efforts in applying these new methods. We will combine aspects of artificial intelligence and deep machine learning methods to applications in chemistry.

Applications for oral and poster presentations are welcomed. Posters will be displayed throughout the day and applicants will be asked if they would like to provide a two-minute flash oral presentation when submitting their abstract. Closing dates are 31st January for oral and 13th April for poster submissions.

More details here http://www.maggichurchouseevents.co.uk/bmcs/AI-2018.htm.


Comments

“Found in Translation”: Predicting Outcomes of Complex Organic Chemistry Reactions

 

An interesting paper uses 1,808,938 reactions from the patent literature as a training set to build a model to predict reactions.

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.

There is also a brief video describing the work.


Comments