Molecular graph Self attention and graph convolution for drug discovery
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2020
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/1721.1/124232 |
| _version_ | 1826188080528228352 |
|---|---|
| author | Bair, Annamarie Elizabeth. |
| author2 | Peter Szolovits. |
| author_facet | Peter Szolovits. Bair, Annamarie Elizabeth. |
| author_sort | Bair, Annamarie Elizabeth. |
| collection | MIT |
| description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. |
| first_indexed | 2024-09-23T07:54:17Z |
| format | Thesis |
| id | mit-1721.1/124232 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T07:54:17Z |
| publishDate | 2020 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1242322020-03-25T03:44:05Z Molecular graph Self attention and graph convolution for drug discovery Bair, Annamarie Elizabeth. Peter Szolovits. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. 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, 2019 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 55-57). 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. by Annamarie Elizabeth Bair. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2020-03-24T15:35:30Z 2020-03-24T15:35:30Z 2019 2019 Thesis https://hdl.handle.net/1721.1/124232 1144932843 eng 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 57 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Bair, Annamarie Elizabeth. Molecular graph Self attention and graph convolution for drug discovery |
| title | Molecular graph Self attention and graph convolution for drug discovery |
| title_full | Molecular graph Self attention and graph convolution for drug discovery |
| title_fullStr | Molecular graph Self attention and graph convolution for drug discovery |
| title_full_unstemmed | Molecular graph Self attention and graph convolution for drug discovery |
| title_short | Molecular graph Self attention and graph convolution for drug discovery |
| title_sort | molecular graph self attention and graph convolution for drug discovery |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/124232 |
| work_keys_str_mv | AT bairannamarieelizabeth moleculargraphselfattentionandgraphconvolutionfordrugdiscovery |