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"Found in Translation" – Neural machine translation models for chemical reaction prediction

Main Author: Schwaller, Philippe
Format: info Proceeding Journal
Bahasa: eng
Terbitan: , 2018
Subjects:
Forward Reaction Prediction
Online Access: https://plan.core-apps.com/acsboston18/abstract/eada8bf1-80f2-4b60-8d06-4eaf83748fde When designing experiments chemists face a complex multidimensional optimization problem and have to explore the nearly endless chemical space based on intuition acquired over years of training and experience. Although intensively studied, predicting the success of a chemical transformation remains a major challenge in Organic Chemistry and is often perceived as an art only human experts can do. Exploiting the analogies between human language and Organic Chemistry, neural machine translation models have recently outperformed templated-based methods and achieved state-of-the-art results in predicting the outcome of chemical reactions. In this work, we analyze the results of such fully data-driven neural sequence-to-sequence models, gain insight into the learned reaction mechanisms using a set of classified reactions and outline the potential of attention weight matrices for atom mappings generation.

Internet

https://plan.core-apps.com/acsboston18/abstract/eada8bf1-80f2-4b60-8d06-4eaf83748fde When designing experiments chemists face a complex multidimensional optimization problem and have to explore the nearly endless chemical space based on intuition acquired over years of training and experience. Although intensively studied, predicting the success of a chemical transformation remains a major challenge in Organic Chemistry and is often perceived as an art only human experts can do. Exploiting the analogies between human language and Organic Chemistry, neural machine translation models have recently outperformed templated-based methods and achieved state-of-the-art results in predicting the outcome of chemical reactions. In this work, we analyze the results of such fully data-driven neural sequence-to-sequence models, gain insight into the learned reaction mechanisms using a set of classified reactions and outline the potential of attention weight matrices for atom mappings generation.

Lokasi

Koleksi Cognizance Journal of Multidisciplinary Studies
Gedung Cognizance Journal of Multidisciplinary Studies
Institusi ZAIN Publications
Kota Stockholm
Provinsi INTERNASIONAL
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