Molecular graph Self attention and graph convolution for drug discovery

Drug development is an important, but complicated and expensive process. By utilizing the power of deep learning, we aim to improve the current process of drug development. We model molecules as undirected graphs and use graph convolutions and self-attention to predict molecular properties. With a series of ablation studies, we demonstrate the added value of several key components in our network. We analyze two standard datasets: BBBP, which includes classication data on whether molecules pass the blood-brain barrier, and ClinTox, which includes toxicity information. Using our architecture, we are able to match state of the art performance on the BBBP prediction task.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Bair, Annamarie Elizabeth.

Advisor dc:contributor.advisor

• Peter Szolovits.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/124232

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/124232