Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly

Abstract Deep generative models are attracting attention as a smart molecular design strategy. However, previous models often render molecules with low synthesizability, hindering their real‐world applications. Here, a novel graph‐based conditional generative model which makes molecules by tailoring...
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Autor*in:	Seonghwan Seo [verfasserIn] Jaechang Lim [verfasserIn] Woo Youn Kim [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	deep generative model deep learning molecular design synthesizability

Übergeordnetes Werk:	In: Advanced Science - Wiley, 2015, 10(2023), 8, Seite n/a-n/a
Übergeordnetes Werk:	volume:10 ; year:2023 ; number:8 ; pages:n/a-n/a

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1002/advs.202206674

Katalog-ID:	DOAJ088570320

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520			\|a Abstract Deep generative models are attracting attention as a smart molecular design strategy. However, previous models often render molecules with low synthesizability, hindering their real‐world applications. Here, a novel graph‐based conditional generative model which makes molecules by tailoring retrosynthetically prepared chemical building blocks until achieving target properties in an auto‐regressive fashion is proposed. This strategy improves the synthesizability and property control of the resulting molecules and also helps learn how to select appropriate building blocks and bind them together to achieve target properties. By applying a negative sampling method to the selection process of building blocks, this model overcame a critical limitation of previous fragment‐based models, which can only use molecules from the training set during generation. As a result, the model works equally well with unseen building blocks without sacrificing computational efficiency. It is demonstrated that the model can generate potential inhibitors with high docking scores against the 3CL protease of SARS‐COV‐2.
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allfields	10.1002/advs.202206674 doi (DE-627)DOAJ088570320 (DE-599)DOAJa49e26893ac2462ebc0eef0a6e9bcf00 DE-627 ger DE-627 rakwb eng Seonghwan Seo verfasserin aut Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Deep generative models are attracting attention as a smart molecular design strategy. However, previous models often render molecules with low synthesizability, hindering their real‐world applications. Here, a novel graph‐based conditional generative model which makes molecules by tailoring retrosynthetically prepared chemical building blocks until achieving target properties in an auto‐regressive fashion is proposed. This strategy improves the synthesizability and property control of the resulting molecules and also helps learn how to select appropriate building blocks and bind them together to achieve target properties. By applying a negative sampling method to the selection process of building blocks, this model overcame a critical limitation of previous fragment‐based models, which can only use molecules from the training set during generation. As a result, the model works equally well with unseen building blocks without sacrificing computational efficiency. It is demonstrated that the model can generate potential inhibitors with high docking scores against the 3CL protease of SARS‐COV‐2. deep generative model deep learning molecular design synthesizability Science Q Jaechang Lim verfasserin aut Woo Youn Kim verfasserin aut In Advanced Science Wiley, 2015 10(2023), 8, Seite n/a-n/a (DE-627)817357777 (DE-600)2808093-2 21983844 nnns volume:10 year:2023 number:8 pages:n/a-n/a https://doi.org/10.1002/advs.202206674 kostenfrei https://doaj.org/article/a49e26893ac2462ebc0eef0a6e9bcf00 kostenfrei https://doi.org/10.1002/advs.202206674 kostenfrei https://doaj.org/toc/2198-3844 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 8 n/a-n/a
spelling	10.1002/advs.202206674 doi (DE-627)DOAJ088570320 (DE-599)DOAJa49e26893ac2462ebc0eef0a6e9bcf00 DE-627 ger DE-627 rakwb eng Seonghwan Seo verfasserin aut Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Deep generative models are attracting attention as a smart molecular design strategy. However, previous models often render molecules with low synthesizability, hindering their real‐world applications. Here, a novel graph‐based conditional generative model which makes molecules by tailoring retrosynthetically prepared chemical building blocks until achieving target properties in an auto‐regressive fashion is proposed. This strategy improves the synthesizability and property control of the resulting molecules and also helps learn how to select appropriate building blocks and bind them together to achieve target properties. By applying a negative sampling method to the selection process of building blocks, this model overcame a critical limitation of previous fragment‐based models, which can only use molecules from the training set during generation. As a result, the model works equally well with unseen building blocks without sacrificing computational efficiency. It is demonstrated that the model can generate potential inhibitors with high docking scores against the 3CL protease of SARS‐COV‐2. deep generative model deep learning molecular design synthesizability Science Q Jaechang Lim verfasserin aut Woo Youn Kim verfasserin aut In Advanced Science Wiley, 2015 10(2023), 8, Seite n/a-n/a (DE-627)817357777 (DE-600)2808093-2 21983844 nnns volume:10 year:2023 number:8 pages:n/a-n/a https://doi.org/10.1002/advs.202206674 kostenfrei https://doaj.org/article/a49e26893ac2462ebc0eef0a6e9bcf00 kostenfrei https://doi.org/10.1002/advs.202206674 kostenfrei https://doaj.org/toc/2198-3844 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 8 n/a-n/a
allfields_unstemmed	10.1002/advs.202206674 doi (DE-627)DOAJ088570320 (DE-599)DOAJa49e26893ac2462ebc0eef0a6e9bcf00 DE-627 ger DE-627 rakwb eng Seonghwan Seo verfasserin aut Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Deep generative models are attracting attention as a smart molecular design strategy. However, previous models often render molecules with low synthesizability, hindering their real‐world applications. Here, a novel graph‐based conditional generative model which makes molecules by tailoring retrosynthetically prepared chemical building blocks until achieving target properties in an auto‐regressive fashion is proposed. This strategy improves the synthesizability and property control of the resulting molecules and also helps learn how to select appropriate building blocks and bind them together to achieve target properties. By applying a negative sampling method to the selection process of building blocks, this model overcame a critical limitation of previous fragment‐based models, which can only use molecules from the training set during generation. As a result, the model works equally well with unseen building blocks without sacrificing computational efficiency. It is demonstrated that the model can generate potential inhibitors with high docking scores against the 3CL protease of SARS‐COV‐2. deep generative model deep learning molecular design synthesizability Science Q Jaechang Lim verfasserin aut Woo Youn Kim verfasserin aut In Advanced Science Wiley, 2015 10(2023), 8, Seite n/a-n/a (DE-627)817357777 (DE-600)2808093-2 21983844 nnns volume:10 year:2023 number:8 pages:n/a-n/a https://doi.org/10.1002/advs.202206674 kostenfrei https://doaj.org/article/a49e26893ac2462ebc0eef0a6e9bcf00 kostenfrei https://doi.org/10.1002/advs.202206674 kostenfrei https://doaj.org/toc/2198-3844 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 8 n/a-n/a
allfieldsGer	10.1002/advs.202206674 doi (DE-627)DOAJ088570320 (DE-599)DOAJa49e26893ac2462ebc0eef0a6e9bcf00 DE-627 ger DE-627 rakwb eng Seonghwan Seo verfasserin aut Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Deep generative models are attracting attention as a smart molecular design strategy. However, previous models often render molecules with low synthesizability, hindering their real‐world applications. Here, a novel graph‐based conditional generative model which makes molecules by tailoring retrosynthetically prepared chemical building blocks until achieving target properties in an auto‐regressive fashion is proposed. This strategy improves the synthesizability and property control of the resulting molecules and also helps learn how to select appropriate building blocks and bind them together to achieve target properties. By applying a negative sampling method to the selection process of building blocks, this model overcame a critical limitation of previous fragment‐based models, which can only use molecules from the training set during generation. As a result, the model works equally well with unseen building blocks without sacrificing computational efficiency. It is demonstrated that the model can generate potential inhibitors with high docking scores against the 3CL protease of SARS‐COV‐2. deep generative model deep learning molecular design synthesizability Science Q Jaechang Lim verfasserin aut Woo Youn Kim verfasserin aut In Advanced Science Wiley, 2015 10(2023), 8, Seite n/a-n/a (DE-627)817357777 (DE-600)2808093-2 21983844 nnns volume:10 year:2023 number:8 pages:n/a-n/a https://doi.org/10.1002/advs.202206674 kostenfrei https://doaj.org/article/a49e26893ac2462ebc0eef0a6e9bcf00 kostenfrei https://doi.org/10.1002/advs.202206674 kostenfrei https://doaj.org/toc/2198-3844 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 8 n/a-n/a
allfieldsSound	10.1002/advs.202206674 doi (DE-627)DOAJ088570320 (DE-599)DOAJa49e26893ac2462ebc0eef0a6e9bcf00 DE-627 ger DE-627 rakwb eng Seonghwan Seo verfasserin aut Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Deep generative models are attracting attention as a smart molecular design strategy. However, previous models often render molecules with low synthesizability, hindering their real‐world applications. Here, a novel graph‐based conditional generative model which makes molecules by tailoring retrosynthetically prepared chemical building blocks until achieving target properties in an auto‐regressive fashion is proposed. This strategy improves the synthesizability and property control of the resulting molecules and also helps learn how to select appropriate building blocks and bind them together to achieve target properties. By applying a negative sampling method to the selection process of building blocks, this model overcame a critical limitation of previous fragment‐based models, which can only use molecules from the training set during generation. As a result, the model works equally well with unseen building blocks without sacrificing computational efficiency. It is demonstrated that the model can generate potential inhibitors with high docking scores against the 3CL protease of SARS‐COV‐2. deep generative model deep learning molecular design synthesizability Science Q Jaechang Lim verfasserin aut Woo Youn Kim verfasserin aut In Advanced Science Wiley, 2015 10(2023), 8, Seite n/a-n/a (DE-627)817357777 (DE-600)2808093-2 21983844 nnns volume:10 year:2023 number:8 pages:n/a-n/a https://doi.org/10.1002/advs.202206674 kostenfrei https://doaj.org/article/a49e26893ac2462ebc0eef0a6e9bcf00 kostenfrei https://doi.org/10.1002/advs.202206674 kostenfrei https://doaj.org/toc/2198-3844 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 8 n/a-n/a
language	English
source	In Advanced Science 10(2023), 8, Seite n/a-n/a volume:10 year:2023 number:8 pages:n/a-n/a
sourceStr	In Advanced Science 10(2023), 8, Seite n/a-n/a volume:10 year:2023 number:8 pages:n/a-n/a
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	deep generative model deep learning molecular design synthesizability Science Q
isfreeaccess_bool	true
container_title	Advanced Science
authorswithroles_txt_mv	Seonghwan Seo @@aut@@ Jaechang Lim @@aut@@ Woo Youn Kim @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	817357777
id	DOAJ088570320
language_de	englisch
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author	Seonghwan Seo
spellingShingle	Seonghwan Seo misc deep generative model misc deep learning misc molecular design misc synthesizability misc Science misc Q Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly
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topic_title	Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly deep generative model deep learning molecular design synthesizability
topic	misc deep generative model misc deep learning misc molecular design misc synthesizability misc Science misc Q
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title	Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly
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title_full	Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly
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title_sort	molecular generative model via retrosynthetically prepared chemical building block assembly
title_auth	Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly
abstract	Abstract Deep generative models are attracting attention as a smart molecular design strategy. However, previous models often render molecules with low synthesizability, hindering their real‐world applications. Here, a novel graph‐based conditional generative model which makes molecules by tailoring retrosynthetically prepared chemical building blocks until achieving target properties in an auto‐regressive fashion is proposed. This strategy improves the synthesizability and property control of the resulting molecules and also helps learn how to select appropriate building blocks and bind them together to achieve target properties. By applying a negative sampling method to the selection process of building blocks, this model overcame a critical limitation of previous fragment‐based models, which can only use molecules from the training set during generation. As a result, the model works equally well with unseen building blocks without sacrificing computational efficiency. It is demonstrated that the model can generate potential inhibitors with high docking scores against the 3CL protease of SARS‐COV‐2.
abstractGer	Abstract Deep generative models are attracting attention as a smart molecular design strategy. However, previous models often render molecules with low synthesizability, hindering their real‐world applications. Here, a novel graph‐based conditional generative model which makes molecules by tailoring retrosynthetically prepared chemical building blocks until achieving target properties in an auto‐regressive fashion is proposed. This strategy improves the synthesizability and property control of the resulting molecules and also helps learn how to select appropriate building blocks and bind them together to achieve target properties. By applying a negative sampling method to the selection process of building blocks, this model overcame a critical limitation of previous fragment‐based models, which can only use molecules from the training set during generation. As a result, the model works equally well with unseen building blocks without sacrificing computational efficiency. It is demonstrated that the model can generate potential inhibitors with high docking scores against the 3CL protease of SARS‐COV‐2.
abstract_unstemmed	Abstract Deep generative models are attracting attention as a smart molecular design strategy. However, previous models often render molecules with low synthesizability, hindering their real‐world applications. Here, a novel graph‐based conditional generative model which makes molecules by tailoring retrosynthetically prepared chemical building blocks until achieving target properties in an auto‐regressive fashion is proposed. This strategy improves the synthesizability and property control of the resulting molecules and also helps learn how to select appropriate building blocks and bind them together to achieve target properties. By applying a negative sampling method to the selection process of building blocks, this model overcame a critical limitation of previous fragment‐based models, which can only use molecules from the training set during generation. As a result, the model works equally well with unseen building blocks without sacrificing computational efficiency. It is demonstrated that the model can generate potential inhibitors with high docking scores against the 3CL protease of SARS‐COV‐2.
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title_short	Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly
url	https://doi.org/10.1002/advs.202206674 https://doaj.org/article/a49e26893ac2462ebc0eef0a6e9bcf00 https://doaj.org/toc/2198-3844
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Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly

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