Cambridge Open Engage home
What is Cambridge Open Engage?
How to Submit
Browse
About
News [opens in a new tab]

Prediction of Chemical Reactions Using Statistical Models of Chemical Knowledge

10 August 2018, Version 1
Working Paper
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Is chemistry discoverable or can it only be invented? – this is the question of a computer scientist and a philosopher of science when looking at application of artificial intelligence methods for developing new chemical entities and new chemical transformations. This study confirms that, at least today, chemistry is, in part, discoverable from past history of chemical research – the accumulated chemical data contains hidden rules of chemistry, which can be exploited to discover new reaction pathways. This is shown using a stochastic block model approach, trained on chemical reaction data obtained from Reaxys®.

Keywords

Reaction Networks
Graph theory
Reaction prediction
Stochastic Block Models

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.

Version History

Aug 10, 2018 Version 1

Version Notes

This version will be updated with a 2nd validation method and correcting some minor errors in the text and examples shown.

Metrics

5,113
1,492
2
Views
Downloads

License

CC logo
CC
BY logo
BY
The content is available under CC BY 4.0 [opens in a new tab]

DOI

10.26434/chemrxiv.6954908.v1
D O I: 10.26434/chemrxiv.6954908.v1 [opens in a new tab]

Funding

P.-M. Jacob would like to thank Peterhouse and the University of Cambridge for funding in the form of a PhD studentship. We gratefully acknowledge collaboration with RELX Intellectual Properties SA, Elsevier and their technical support, which enabled us to mine Reaxys. This work was funded, in part, by the EPSRC project “Terpene-based manufacturing for sustainable chemical feedstocks” EP/K014889. This project is funded in part by the National Research Foundation (NRF), Prime Minister’s Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) program as a part of the Cambridge Centre for Advanced Research and Education in Singapore Ltd (CARES).

Author’s competing interest statement

Authors declare no conflict of interest

Share