A review on compound-protein interaction prediction methods: Data, format, representation and model
There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been ac...
Ausführliche Beschreibung
Autor*in: |
Sangsoo Lim [verfasserIn] Yijingxiu Lu [verfasserIn] Chang Yun Cho [verfasserIn] Inyoung Sung [verfasserIn] Jungwoo Kim [verfasserIn] Youngkuk Kim [verfasserIn] Sungjoon Park [verfasserIn] Sun Kim [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2021 |
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Übergeordnetes Werk: |
In: Computational and Structural Biotechnology Journal - Elsevier, 2013, 19(2021), Seite 1541-1556 |
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Übergeordnetes Werk: |
volume:19 ; year:2021 ; pages:1541-1556 |
Links: |
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DOI / URN: |
10.1016/j.csbj.2021.03.004 |
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Katalog-ID: |
DOAJ019082924 |
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520 | |a There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods. | ||
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10.1016/j.csbj.2021.03.004 doi (DE-627)DOAJ019082924 (DE-599)DOAJc02a7507566b4e85af7cec5e7835789f DE-627 ger DE-627 rakwb eng TP248.13-248.65 Sangsoo Lim verfasserin aut A review on compound-protein interaction prediction methods: Data, format, representation and model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods. Compound-protein interaction Data representation Interpretable learning Chemical descriptors Protein descriptors Machine learning Biotechnology Yijingxiu Lu verfasserin aut Chang Yun Cho verfasserin aut Inyoung Sung verfasserin aut Jungwoo Kim verfasserin aut Youngkuk Kim verfasserin aut Sungjoon Park verfasserin aut Sun Kim verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 19(2021), Seite 1541-1556 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:19 year:2021 pages:1541-1556 https://doi.org/10.1016/j.csbj.2021.03.004 kostenfrei https://doaj.org/article/c02a7507566b4e85af7cec5e7835789f kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037021000763 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2021 1541-1556 |
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10.1016/j.csbj.2021.03.004 doi (DE-627)DOAJ019082924 (DE-599)DOAJc02a7507566b4e85af7cec5e7835789f DE-627 ger DE-627 rakwb eng TP248.13-248.65 Sangsoo Lim verfasserin aut A review on compound-protein interaction prediction methods: Data, format, representation and model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods. Compound-protein interaction Data representation Interpretable learning Chemical descriptors Protein descriptors Machine learning Biotechnology Yijingxiu Lu verfasserin aut Chang Yun Cho verfasserin aut Inyoung Sung verfasserin aut Jungwoo Kim verfasserin aut Youngkuk Kim verfasserin aut Sungjoon Park verfasserin aut Sun Kim verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 19(2021), Seite 1541-1556 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:19 year:2021 pages:1541-1556 https://doi.org/10.1016/j.csbj.2021.03.004 kostenfrei https://doaj.org/article/c02a7507566b4e85af7cec5e7835789f kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037021000763 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2021 1541-1556 |
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10.1016/j.csbj.2021.03.004 doi (DE-627)DOAJ019082924 (DE-599)DOAJc02a7507566b4e85af7cec5e7835789f DE-627 ger DE-627 rakwb eng TP248.13-248.65 Sangsoo Lim verfasserin aut A review on compound-protein interaction prediction methods: Data, format, representation and model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods. Compound-protein interaction Data representation Interpretable learning Chemical descriptors Protein descriptors Machine learning Biotechnology Yijingxiu Lu verfasserin aut Chang Yun Cho verfasserin aut Inyoung Sung verfasserin aut Jungwoo Kim verfasserin aut Youngkuk Kim verfasserin aut Sungjoon Park verfasserin aut Sun Kim verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 19(2021), Seite 1541-1556 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:19 year:2021 pages:1541-1556 https://doi.org/10.1016/j.csbj.2021.03.004 kostenfrei https://doaj.org/article/c02a7507566b4e85af7cec5e7835789f kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037021000763 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2021 1541-1556 |
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10.1016/j.csbj.2021.03.004 doi (DE-627)DOAJ019082924 (DE-599)DOAJc02a7507566b4e85af7cec5e7835789f DE-627 ger DE-627 rakwb eng TP248.13-248.65 Sangsoo Lim verfasserin aut A review on compound-protein interaction prediction methods: Data, format, representation and model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods. Compound-protein interaction Data representation Interpretable learning Chemical descriptors Protein descriptors Machine learning Biotechnology Yijingxiu Lu verfasserin aut Chang Yun Cho verfasserin aut Inyoung Sung verfasserin aut Jungwoo Kim verfasserin aut Youngkuk Kim verfasserin aut Sungjoon Park verfasserin aut Sun Kim verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 19(2021), Seite 1541-1556 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:19 year:2021 pages:1541-1556 https://doi.org/10.1016/j.csbj.2021.03.004 kostenfrei https://doaj.org/article/c02a7507566b4e85af7cec5e7835789f kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037021000763 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2021 1541-1556 |
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10.1016/j.csbj.2021.03.004 doi (DE-627)DOAJ019082924 (DE-599)DOAJc02a7507566b4e85af7cec5e7835789f DE-627 ger DE-627 rakwb eng TP248.13-248.65 Sangsoo Lim verfasserin aut A review on compound-protein interaction prediction methods: Data, format, representation and model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods. Compound-protein interaction Data representation Interpretable learning Chemical descriptors Protein descriptors Machine learning Biotechnology Yijingxiu Lu verfasserin aut Chang Yun Cho verfasserin aut Inyoung Sung verfasserin aut Jungwoo Kim verfasserin aut Youngkuk Kim verfasserin aut Sungjoon Park verfasserin aut Sun Kim verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 19(2021), Seite 1541-1556 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:19 year:2021 pages:1541-1556 https://doi.org/10.1016/j.csbj.2021.03.004 kostenfrei https://doaj.org/article/c02a7507566b4e85af7cec5e7835789f kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037021000763 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2021 1541-1556 |
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A review on compound-protein interaction prediction methods: Data, format, representation and model |
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There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods. |
abstractGer |
There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods. |
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There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods. |
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