CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Generating molecules via chemical reactions
Authors
,
J Bradshaw
+4 more
JM Hernández-Lobato
MJ Kusner
B Paige
MHS Segler
Publication date
1 January 2019
Publisher
Abstract
© Deep Generative Models for Highly Structured Data, DGS@ICLR 2019 Workshop.All right reserved. Over the last few years exciting work in deep generative models has produced models able to suggest new organic molecules by generating strings, trees, and graphs representing their structure. While such models are able to generate molecules with desirable properties, their utility in practice is limited due to the difficulty in knowing how to synthesize these molecules. We therefore propose a new molecule generation model, mirroring a more realistic real-world process, where reactants are selected and combined to form more complex molecules. More specifically, our generative model proposes a bag of initial reactants (selected from a pool of commercially-available molecules) and uses a reaction model to predict how they react together to generate new molecules. Modeling the entire process of constructing a molecule during generation offers a number of advantages. First, we show that such a model has the ability to generate a wide, diverse set of valid and unique molecules due to the useful inductive biases of modeling reactions. Second, modeling synthesis routes rather than final molecules offers practical advantages to chemists who are not only interested in new molecules but also suggestions on stable and safe synthetic routes. Third, we demonstrate the capabilities of our model to also solve one-step retrosynthesis problems, predicting a set of reactants that can produce a target product
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
CUED - Cambridge University Engineering Department
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:generic.eprints.org:111972...
Last time updated on 15/07/2020
UCL Discovery
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:eprints.ucl.ac.uk.OAI2:100...
Last time updated on 26/05/2020
CUED - Cambridge University Engineering Department
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:generic.eprints.org:119562...
Last time updated on 03/12/2020