Predicting the Feasibility of Copper(I)-Catalyzed Alkyne–Azide Cycloaddition Reactions Using a Recurrent Neural Network with a Self-Attention Mechanism
The copper(I)-catalyzed alkyne–azide cycloaddition (CuAAC) reaction, a major click chemistry reaction, is widely employed in drug discovery and chemical biology. However, the success rate of the CuAAC reaction is not satisfactory as expected, and in order to improve its performance, we developed a...
Gespeichert in:
Veröffentlicht in: | Journal of chemical information and modeling 2020-03, Vol.60 (3), p.1165-1174 |
---|---|
Hauptverfasser: | , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 1174 |
---|---|
container_issue | 3 |
container_start_page | 1165 |
container_title | Journal of chemical information and modeling |
container_volume | 60 |
creator | Su, Shimin Yang, Yuyao Gan, Hanlin Zheng, Shuangjia Gu, Fenglong Zhao, Chao Xu, Jun |
description | The copper(I)-catalyzed alkyne–azide cycloaddition (CuAAC) reaction, a major click chemistry reaction, is widely employed in drug discovery and chemical biology. However, the success rate of the CuAAC reaction is not satisfactory as expected, and in order to improve its performance, we developed a recurrent neural network (RNN) model to predict its feasibility. First, we designed and synthesized a structurally diverse library of 700 compounds with the CuAAC reaction to obtain experimental data. Then, using reaction SMILES as input, we generated a bidirectional long–short-term memory with a self-attention mechanism (BiLSTM-SA) model. Our best prediction model has total accuracy of 80%. With the self-attention mechanism, adverse substructures responsible for negative reactions were recognized and derived as quantitative descriptors. Density functional theory investigations were conducted to provide evidence for the correlation between bromo-α-C hybrid types and the success rate of the reaction. Quantitative descriptors combined with RDKit descriptors were fed to three machine learning models, a support vector machine, random forest, and logistic regression, and resulted in improved performance. The BiLSTM-SA model for predicting the feasibility of the CuAAC reaction is superior to other conventional learning methods and advances heuristic chemical rules. |
doi_str_mv | 10.1021/acs.jcim.9b00929 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2350907114</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2350907114</sourcerecordid><originalsourceid>FETCH-LOGICAL-a364t-f0984d79e06a0eb7870d925d7cef5a410df0f8e5e5315254289c5fab8e9e70a23</originalsourceid><addsrcrecordid>eNp1kc1u1DAURiMEoqWwZ4UssSlSM1w7cRIvRxGlldqCgErsIse-YTzNz9R2VKUr3oFVX69PgsPMsEBida_s8x1b-qLoNYUFBUbfS-UWa2W6hagBBBNPokPKUxGLDL4_3e9cZAfRC-fWAEkiMvY8OkgY0CSl4jB6-GxRG-VN_4P4FZJTlM7UpjV-IkNDymGzQXt8_i4upZftdI-aLNubqcfHn7-W90YjKSfVDlJr483Qky8o1bw4cu1mpwwnarQWe0-ucLSyDcPfDfaG3Bm_CvdfsW3ipfeBmAWXqFayN657GT1rZOvw1W4eRdenH76VZ_HFp4_n5fIilkmW-rgBUaQ6FwiZBKzzIgctGNe5wobLlIJuoCmQI08oZzxlhVC8kXWBAnOQLDmKjrfejR1uR3S-6oxT2Layx2F0FUs4CMgpTQP69h90PYy2D78LlIAs4zmHQMGWUnZwzmJTbazppJ0qCtVcWxVqq-baql1tIfJmJx7rDvXfwL6nAJxsgT_R_aP_9f0GuRSmTQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2390665750</pqid></control><display><type>article</type><title>Predicting the Feasibility of Copper(I)-Catalyzed Alkyne–Azide Cycloaddition Reactions Using a Recurrent Neural Network with a Self-Attention Mechanism</title><source>ACS Publications</source><creator>Su, Shimin ; Yang, Yuyao ; Gan, Hanlin ; Zheng, Shuangjia ; Gu, Fenglong ; Zhao, Chao ; Xu, Jun</creator><creatorcontrib>Su, Shimin ; Yang, Yuyao ; Gan, Hanlin ; Zheng, Shuangjia ; Gu, Fenglong ; Zhao, Chao ; Xu, Jun</creatorcontrib><description>The copper(I)-catalyzed alkyne–azide cycloaddition (CuAAC) reaction, a major click chemistry reaction, is widely employed in drug discovery and chemical biology. However, the success rate of the CuAAC reaction is not satisfactory as expected, and in order to improve its performance, we developed a recurrent neural network (RNN) model to predict its feasibility. First, we designed and synthesized a structurally diverse library of 700 compounds with the CuAAC reaction to obtain experimental data. Then, using reaction SMILES as input, we generated a bidirectional long–short-term memory with a self-attention mechanism (BiLSTM-SA) model. Our best prediction model has total accuracy of 80%. With the self-attention mechanism, adverse substructures responsible for negative reactions were recognized and derived as quantitative descriptors. Density functional theory investigations were conducted to provide evidence for the correlation between bromo-α-C hybrid types and the success rate of the reaction. Quantitative descriptors combined with RDKit descriptors were fed to three machine learning models, a support vector machine, random forest, and logistic regression, and resulted in improved performance. The BiLSTM-SA model for predicting the feasibility of the CuAAC reaction is superior to other conventional learning methods and advances heuristic chemical rules.</description><identifier>ISSN: 1549-9596</identifier><identifier>EISSN: 1549-960X</identifier><identifier>DOI: 10.1021/acs.jcim.9b00929</identifier><identifier>PMID: 32013419</identifier><language>eng</language><publisher>United States: American Chemical Society</publisher><subject>Alkynes ; Chemical reactions ; Chemical synthesis ; Copper ; Cycloaddition ; Density functional theory ; Feasibility ; Heuristic methods ; Machine learning ; Model accuracy ; Neural networks ; Prediction models ; Recurrent neural networks ; Substructures ; Support vector machines</subject><ispartof>Journal of chemical information and modeling, 2020-03, Vol.60 (3), p.1165-1174</ispartof><rights>Copyright American Chemical Society Mar 23, 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a364t-f0984d79e06a0eb7870d925d7cef5a410df0f8e5e5315254289c5fab8e9e70a23</citedby><cites>FETCH-LOGICAL-a364t-f0984d79e06a0eb7870d925d7cef5a410df0f8e5e5315254289c5fab8e9e70a23</cites><orcidid>0000-0002-4628-2440 ; 0000-0002-1075-0337 ; 0000-0001-5356-0157 ; 0000-0001-9747-4285</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://pubs.acs.org/doi/pdf/10.1021/acs.jcim.9b00929$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/acs.jcim.9b00929$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>314,777,781,2752,27057,27905,27906,56719,56769</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32013419$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Su, Shimin</creatorcontrib><creatorcontrib>Yang, Yuyao</creatorcontrib><creatorcontrib>Gan, Hanlin</creatorcontrib><creatorcontrib>Zheng, Shuangjia</creatorcontrib><creatorcontrib>Gu, Fenglong</creatorcontrib><creatorcontrib>Zhao, Chao</creatorcontrib><creatorcontrib>Xu, Jun</creatorcontrib><title>Predicting the Feasibility of Copper(I)-Catalyzed Alkyne–Azide Cycloaddition Reactions Using a Recurrent Neural Network with a Self-Attention Mechanism</title><title>Journal of chemical information and modeling</title><addtitle>J. Chem. Inf. Model</addtitle><description>The copper(I)-catalyzed alkyne–azide cycloaddition (CuAAC) reaction, a major click chemistry reaction, is widely employed in drug discovery and chemical biology. However, the success rate of the CuAAC reaction is not satisfactory as expected, and in order to improve its performance, we developed a recurrent neural network (RNN) model to predict its feasibility. First, we designed and synthesized a structurally diverse library of 700 compounds with the CuAAC reaction to obtain experimental data. Then, using reaction SMILES as input, we generated a bidirectional long–short-term memory with a self-attention mechanism (BiLSTM-SA) model. Our best prediction model has total accuracy of 80%. With the self-attention mechanism, adverse substructures responsible for negative reactions were recognized and derived as quantitative descriptors. Density functional theory investigations were conducted to provide evidence for the correlation between bromo-α-C hybrid types and the success rate of the reaction. Quantitative descriptors combined with RDKit descriptors were fed to three machine learning models, a support vector machine, random forest, and logistic regression, and resulted in improved performance. The BiLSTM-SA model for predicting the feasibility of the CuAAC reaction is superior to other conventional learning methods and advances heuristic chemical rules.</description><subject>Alkynes</subject><subject>Chemical reactions</subject><subject>Chemical synthesis</subject><subject>Copper</subject><subject>Cycloaddition</subject><subject>Density functional theory</subject><subject>Feasibility</subject><subject>Heuristic methods</subject><subject>Machine learning</subject><subject>Model accuracy</subject><subject>Neural networks</subject><subject>Prediction models</subject><subject>Recurrent neural networks</subject><subject>Substructures</subject><subject>Support vector machines</subject><issn>1549-9596</issn><issn>1549-960X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp1kc1u1DAURiMEoqWwZ4UssSlSM1w7cRIvRxGlldqCgErsIse-YTzNz9R2VKUr3oFVX69PgsPMsEBida_s8x1b-qLoNYUFBUbfS-UWa2W6hagBBBNPokPKUxGLDL4_3e9cZAfRC-fWAEkiMvY8OkgY0CSl4jB6-GxRG-VN_4P4FZJTlM7UpjV-IkNDymGzQXt8_i4upZftdI-aLNubqcfHn7-W90YjKSfVDlJr483Qky8o1bw4cu1mpwwnarQWe0-ucLSyDcPfDfaG3Bm_CvdfsW3ipfeBmAWXqFayN657GT1rZOvw1W4eRdenH76VZ_HFp4_n5fIilkmW-rgBUaQ6FwiZBKzzIgctGNe5wobLlIJuoCmQI08oZzxlhVC8kXWBAnOQLDmKjrfejR1uR3S-6oxT2Layx2F0FUs4CMgpTQP69h90PYy2D78LlIAs4zmHQMGWUnZwzmJTbazppJ0qCtVcWxVqq-baql1tIfJmJx7rDvXfwL6nAJxsgT_R_aP_9f0GuRSmTQ</recordid><startdate>20200323</startdate><enddate>20200323</enddate><creator>Su, Shimin</creator><creator>Yang, Yuyao</creator><creator>Gan, Hanlin</creator><creator>Zheng, Shuangjia</creator><creator>Gu, Fenglong</creator><creator>Zhao, Chao</creator><creator>Xu, Jun</creator><general>American Chemical Society</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SR</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-4628-2440</orcidid><orcidid>https://orcid.org/0000-0002-1075-0337</orcidid><orcidid>https://orcid.org/0000-0001-5356-0157</orcidid><orcidid>https://orcid.org/0000-0001-9747-4285</orcidid></search><sort><creationdate>20200323</creationdate><title>Predicting the Feasibility of Copper(I)-Catalyzed Alkyne–Azide Cycloaddition Reactions Using a Recurrent Neural Network with a Self-Attention Mechanism</title><author>Su, Shimin ; Yang, Yuyao ; Gan, Hanlin ; Zheng, Shuangjia ; Gu, Fenglong ; Zhao, Chao ; Xu, Jun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a364t-f0984d79e06a0eb7870d925d7cef5a410df0f8e5e5315254289c5fab8e9e70a23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Alkynes</topic><topic>Chemical reactions</topic><topic>Chemical synthesis</topic><topic>Copper</topic><topic>Cycloaddition</topic><topic>Density functional theory</topic><topic>Feasibility</topic><topic>Heuristic methods</topic><topic>Machine learning</topic><topic>Model accuracy</topic><topic>Neural networks</topic><topic>Prediction models</topic><topic>Recurrent neural networks</topic><topic>Substructures</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Su, Shimin</creatorcontrib><creatorcontrib>Yang, Yuyao</creatorcontrib><creatorcontrib>Gan, Hanlin</creatorcontrib><creatorcontrib>Zheng, Shuangjia</creatorcontrib><creatorcontrib>Gu, Fenglong</creatorcontrib><creatorcontrib>Zhao, Chao</creatorcontrib><creatorcontrib>Xu, Jun</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of chemical information and modeling</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Su, Shimin</au><au>Yang, Yuyao</au><au>Gan, Hanlin</au><au>Zheng, Shuangjia</au><au>Gu, Fenglong</au><au>Zhao, Chao</au><au>Xu, Jun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting the Feasibility of Copper(I)-Catalyzed Alkyne–Azide Cycloaddition Reactions Using a Recurrent Neural Network with a Self-Attention Mechanism</atitle><jtitle>Journal of chemical information and modeling</jtitle><addtitle>J. Chem. Inf. Model</addtitle><date>2020-03-23</date><risdate>2020</risdate><volume>60</volume><issue>3</issue><spage>1165</spage><epage>1174</epage><pages>1165-1174</pages><issn>1549-9596</issn><eissn>1549-960X</eissn><abstract>The copper(I)-catalyzed alkyne–azide cycloaddition (CuAAC) reaction, a major click chemistry reaction, is widely employed in drug discovery and chemical biology. However, the success rate of the CuAAC reaction is not satisfactory as expected, and in order to improve its performance, we developed a recurrent neural network (RNN) model to predict its feasibility. First, we designed and synthesized a structurally diverse library of 700 compounds with the CuAAC reaction to obtain experimental data. Then, using reaction SMILES as input, we generated a bidirectional long–short-term memory with a self-attention mechanism (BiLSTM-SA) model. Our best prediction model has total accuracy of 80%. With the self-attention mechanism, adverse substructures responsible for negative reactions were recognized and derived as quantitative descriptors. Density functional theory investigations were conducted to provide evidence for the correlation between bromo-α-C hybrid types and the success rate of the reaction. Quantitative descriptors combined with RDKit descriptors were fed to three machine learning models, a support vector machine, random forest, and logistic regression, and resulted in improved performance. The BiLSTM-SA model for predicting the feasibility of the CuAAC reaction is superior to other conventional learning methods and advances heuristic chemical rules.</abstract><cop>United States</cop><pub>American Chemical Society</pub><pmid>32013419</pmid><doi>10.1021/acs.jcim.9b00929</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-4628-2440</orcidid><orcidid>https://orcid.org/0000-0002-1075-0337</orcidid><orcidid>https://orcid.org/0000-0001-5356-0157</orcidid><orcidid>https://orcid.org/0000-0001-9747-4285</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1549-9596 |
ispartof | Journal of chemical information and modeling, 2020-03, Vol.60 (3), p.1165-1174 |
issn | 1549-9596 1549-960X |
language | eng |
recordid | cdi_proquest_miscellaneous_2350907114 |
source | ACS Publications |
subjects | Alkynes Chemical reactions Chemical synthesis Copper Cycloaddition Density functional theory Feasibility Heuristic methods Machine learning Model accuracy Neural networks Prediction models Recurrent neural networks Substructures Support vector machines |
title | Predicting the Feasibility of Copper(I)-Catalyzed Alkyne–Azide Cycloaddition Reactions Using a Recurrent Neural Network with a Self-Attention Mechanism |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-20T01%3A17%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Predicting%20the%20Feasibility%20of%20Copper(I)-Catalyzed%20Alkyne%E2%80%93Azide%20Cycloaddition%20Reactions%20Using%20a%20Recurrent%20Neural%20Network%20with%20a%20Self-Attention%20Mechanism&rft.jtitle=Journal%20of%20chemical%20information%20and%20modeling&rft.au=Su,%20Shimin&rft.date=2020-03-23&rft.volume=60&rft.issue=3&rft.spage=1165&rft.epage=1174&rft.pages=1165-1174&rft.issn=1549-9596&rft.eissn=1549-960X&rft_id=info:doi/10.1021/acs.jcim.9b00929&rft_dat=%3Cproquest_cross%3E2350907114%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2390665750&rft_id=info:pmid/32013419&rfr_iscdi=true |