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Generative models for graph-based protein design
dc.contributor.author | Ingraham, John | |
dc.contributor.author | Garg, Vikas Kamur | |
dc.contributor.author | Barzilay, Regina | |
dc.contributor.author | Jaakkola, Tommi S | |
dc.date.accessioned | 2021-02-09T22:25:50Z | |
dc.date.available | 2021-02-09T22:25:50Z | |
dc.identifier.uri | https://hdl.handle.net/1721.1/129731 | |
dc.description.abstract | Engineered proteins offer the potential to solve many problems in biomedicine, energy, and materials science, but creating designs that succeed is difficult in practice. A significant aspect of this challenge is the complex coupling between protein sequence and 3D structure, with the task of finding a viable design often referred to as the inverse protein folding problem. In this work, we introduce a conditional generative model for protein sequences given 3D structures based on graph representations. Our approach efficiently captures the complex dependencies in proteins by focusing on those that are long-range in sequence but local in 3D space. This graph-based approach improves in both speed and reliability over conventional and other neural network-based approaches, and takes a step toward rapid and targeted biomolecular design with the aid of deep generative models. | en_US |
dc.language.iso | en | |
dc.publisher | Neural Information Processing Systems Foundation, Inc. | en_US |
dc.relation.isversionof | https://papers.nips.cc/paper/2019 | en_US |
dc.rights | Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. | en_US |
dc.source | Neural Information Processing Systems (NIPS) | en_US |
dc.title | Generative models for graph-based protein design | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Ingraham, John et al. "Generative models for graph-based protein design." Advances in Neural Information Processing Systems 32 (NeurIPS 2019), December 2019, Vancouver, Canada, Neural Information Processing Systems Foundation, 2019. © 2019 Neural Information Processing Systems Foundation | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | en_US |
dc.relation.journal | Advances in Neural Information Processing Systems 32 (NeurIPS 2019) | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
dc.date.updated | 2020-12-01T16:19:09Z | |
dspace.orderedauthors | Ingraham, J; Garg, VK; Barzilay, R; Jaakkola, T | en_US |
dspace.date.submission | 2020-12-01T16:19:18Z | |
mit.journal.volume | 32 | en_US |
mit.license | PUBLISHER_POLICY |