MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Scalability and interpretability of graph neural networks for small molecules

Author(s)
Sangha, Manjot.
Thumbnail
Download1098180075-MIT.pdf (762.2Kb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Joseph Jacobson.
Terms of use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582
Metadata
Show full item record
Abstract
In this thesis I examine the use of graph neural networks for prediction tasks in chemistry with an emphasis on interpretable and scalable methods. I propose a novel kernel-inspired graph neural network architecture, called a subgraph matching neural network (SMNN), which is designed to have all feature representations and weights be human interpretable. I show that this network can achieve competitive performance with common graph neural network baselines. I also show that the network is capable of learning features that allow for transfer learning to larger molecules with significantly better performance than some baselines. This provides evidence the network is learning chemically useful representations. I then propose a framework for defining graph pooling operations to improve the scalability of graph neural networks with molecule size. I empirically examine some examples of these graph pooling layers and show that they can provide a significant speed-up without hurting accuracy, and even improving accuracy in some cases. Finally an instance of the SMNN network with a pooling layer is shown to achieve state-of-the-art accuracy on the Harvard Clean Energy Project dataset.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
 
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018
 
Cataloged from student-submitted PDF version of thesis.
 
Includes bibliographical references (pages 95-98).
 
Date issued
2018
URI
https://hdl.handle.net/1721.1/121639
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

Collections
  • Graduate Theses

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.