Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions

We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaini...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:arXiv.org 2020-07
Hauptverfasser: Ryou, Serim, Maser, Michael R, Cui, Alexander Y, DeLano, Travis J, Yue, Yisong, Reisman, Sarah E
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Ryou, Serim
Maser, Michael R
Cui, Alexander Y
DeLano, Travis J
Yue, Yisong
Reisman, Sarah E
description We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning.
format Article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2422276407</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2422276407</sourcerecordid><originalsourceid>FETCH-proquest_journals_24222764073</originalsourceid><addsrcrecordid>eNqNjrsKwjAYRoMgWLTvEHAuxKQX9-JlUrFOLiW2iU0tSf2T4Osb0QdwOge-M3wTFFHGVsk6pXSGYmt7QgjNC5plLELXHfCxwwfhgQ8B7mXgYbE0gF0n8AlEqxqnjMZG4srfrAPuRFKNolFSNfgId64Dz4J_s9LoVn3MLtBU8sGK-Mc5Wm43l3KfjGCeXlhX98aDDlNNwzda5Ckp2H_VGyZjQq0</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2422276407</pqid></control><display><type>article</type><title>Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions</title><source>Free E- Journals</source><creator>Ryou, Serim ; Maser, Michael R ; Cui, Alexander Y ; DeLano, Travis J ; Yue, Yisong ; Reisman, Sarah E</creator><creatorcontrib>Ryou, Serim ; Maser, Michael R ; Cui, Alexander Y ; DeLano, Travis J ; Yue, Yisong ; Reisman, Sarah E</creatorcontrib><description>We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Chemical reactions ; Graph neural networks ; Machine learning ; Molecular machines ; Neural networks ; Organic chemicals ; Organic chemistry ; Reagents ; Substrates</subject><ispartof>arXiv.org, 2020-07</ispartof><rights>2020. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,784</link.rule.ids></links><search><creatorcontrib>Ryou, Serim</creatorcontrib><creatorcontrib>Maser, Michael R</creatorcontrib><creatorcontrib>Cui, Alexander Y</creatorcontrib><creatorcontrib>DeLano, Travis J</creatorcontrib><creatorcontrib>Yue, Yisong</creatorcontrib><creatorcontrib>Reisman, Sarah E</creatorcontrib><title>Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions</title><title>arXiv.org</title><description>We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning.</description><subject>Chemical reactions</subject><subject>Graph neural networks</subject><subject>Machine learning</subject><subject>Molecular machines</subject><subject>Neural networks</subject><subject>Organic chemicals</subject><subject>Organic chemistry</subject><subject>Reagents</subject><subject>Substrates</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNjrsKwjAYRoMgWLTvEHAuxKQX9-JlUrFOLiW2iU0tSf2T4Osb0QdwOge-M3wTFFHGVsk6pXSGYmt7QgjNC5plLELXHfCxwwfhgQ8B7mXgYbE0gF0n8AlEqxqnjMZG4srfrAPuRFKNolFSNfgId64Dz4J_s9LoVn3MLtBU8sGK-Mc5Wm43l3KfjGCeXlhX98aDDlNNwzda5Ckp2H_VGyZjQq0</recordid><startdate>20200709</startdate><enddate>20200709</enddate><creator>Ryou, Serim</creator><creator>Maser, Michael R</creator><creator>Cui, Alexander Y</creator><creator>DeLano, Travis J</creator><creator>Yue, Yisong</creator><creator>Reisman, Sarah E</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20200709</creationdate><title>Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions</title><author>Ryou, Serim ; Maser, Michael R ; Cui, Alexander Y ; DeLano, Travis J ; Yue, Yisong ; Reisman, Sarah E</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_24222764073</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Chemical reactions</topic><topic>Graph neural networks</topic><topic>Machine learning</topic><topic>Molecular machines</topic><topic>Neural networks</topic><topic>Organic chemicals</topic><topic>Organic chemistry</topic><topic>Reagents</topic><topic>Substrates</topic><toplevel>online_resources</toplevel><creatorcontrib>Ryou, Serim</creatorcontrib><creatorcontrib>Maser, Michael R</creatorcontrib><creatorcontrib>Cui, Alexander Y</creatorcontrib><creatorcontrib>DeLano, Travis J</creatorcontrib><creatorcontrib>Yue, Yisong</creatorcontrib><creatorcontrib>Reisman, Sarah E</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ryou, Serim</au><au>Maser, Michael R</au><au>Cui, Alexander Y</au><au>DeLano, Travis J</au><au>Yue, Yisong</au><au>Reisman, Sarah E</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions</atitle><jtitle>arXiv.org</jtitle><date>2020-07-09</date><risdate>2020</risdate><eissn>2331-8422</eissn><abstract>We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2020-07
issn 2331-8422
language eng
recordid cdi_proquest_journals_2422276407
source Free E- Journals
subjects Chemical reactions
Graph neural networks
Machine learning
Molecular machines
Neural networks
Organic chemicals
Organic chemistry
Reagents
Substrates
title Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T05%3A28%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Graph%20Neural%20Networks%20for%20the%20Prediction%20of%20Substrate-Specific%20Organic%20Reaction%20Conditions&rft.jtitle=arXiv.org&rft.au=Ryou,%20Serim&rft.date=2020-07-09&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2422276407%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2422276407&rft_id=info:pmid/&rfr_iscdi=true