{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/124232"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/124232","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Molecular graph Self attention and graph convolution for drug discovery","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Bair, Annamarie Elizabeth."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Peter Szolovits."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:20:44Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/124232","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Peter Szolovits."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","EECS"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. 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Using our architecture, we are able to match state of the art performance on the BBBP prediction task."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Molecular graph Self attention and graph convolution for drug discovery"]}]}],"canonical_facts":{"dc:contributor.advisor":["Peter Szolovits."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","EECS"],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Bair, Annamarie Elizabeth."],"dc:date.accessioned":["2020-03-24T15:35:30Z"],"dc:date.available":["2020-03-24T15:35:30Z"],"dc:date.issued":["2019"],"dc:description":["This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Thesis: M. 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