Integrating synthetic accessibility with AI-based generative drug design

Abstract Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize t...
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Autor*in:	Maud Parrot [verfasserIn] Hamza Tajmouati [verfasserIn] Vinicius Barros Ribeiro da Silva [verfasserIn] Brian Ross Atwood [verfasserIn] Robin Fourcade [verfasserIn] Yann Gaston-Mathé [verfasserIn] Nicolas Do Huu [verfasserIn] Quentin Perron [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	In-silico synthesizability Retrosynthesis artificial intelligence machine learning In silico molecular generation

Übergeordnetes Werk:	In: Journal of Cheminformatics - BMC, 2010, 15(2023), 1, Seite 17
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:1 ; pages:17

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s13321-023-00742-8

Katalog-ID:	DOAJ091741920

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520			\|a Abstract Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule “synthesizability”, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic accessibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). We start by comparing several synthetic accessibility scores to a binary “chemist score” as estimated by chemists on a bench of generated molecules, as a first experimental validation that the RScore is a reliable synthetic accessibility score. We then describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables our molecular generators to produce more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on ( https://github.com/iktos/generation-under-synthetic-constraint ). Graphic Abstract
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allfields	10.1186/s13321-023-00742-8 doi (DE-627)DOAJ091741920 (DE-599)DOAJc00070d8eba741aeb536bbb3910125d4 DE-627 ger DE-627 rakwb eng T58.5-58.64 QD1-999 Maud Parrot verfasserin aut Integrating synthetic accessibility with AI-based generative drug design 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule “synthesizability”, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic accessibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). We start by comparing several synthetic accessibility scores to a binary “chemist score” as estimated by chemists on a bench of generated molecules, as a first experimental validation that the RScore is a reliable synthetic accessibility score. We then describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables our molecular generators to produce more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on ( https://github.com/iktos/generation-under-synthetic-constraint ). Graphic Abstract In-silico synthesizability Retrosynthesis artificial intelligence machine learning In silico molecular generation Information technology Chemistry Hamza Tajmouati verfasserin aut Vinicius Barros Ribeiro da Silva verfasserin aut Brian Ross Atwood verfasserin aut Robin Fourcade verfasserin aut Yann Gaston-Mathé verfasserin aut Nicolas Do Huu verfasserin aut Quentin Perron verfasserin aut In Journal of Cheminformatics BMC, 2010 15(2023), 1, Seite 17 (DE-627)594779219 (DE-600)2486539-4 17582946 nnns volume:15 year:2023 number:1 pages:17 https://doi.org/10.1186/s13321-023-00742-8 kostenfrei https://doaj.org/article/c00070d8eba741aeb536bbb3910125d4 kostenfrei https://doi.org/10.1186/s13321-023-00742-8 kostenfrei https://doaj.org/toc/1758-2946 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 17
spelling	10.1186/s13321-023-00742-8 doi (DE-627)DOAJ091741920 (DE-599)DOAJc00070d8eba741aeb536bbb3910125d4 DE-627 ger DE-627 rakwb eng T58.5-58.64 QD1-999 Maud Parrot verfasserin aut Integrating synthetic accessibility with AI-based generative drug design 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule “synthesizability”, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic accessibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). We start by comparing several synthetic accessibility scores to a binary “chemist score” as estimated by chemists on a bench of generated molecules, as a first experimental validation that the RScore is a reliable synthetic accessibility score. We then describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables our molecular generators to produce more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on ( https://github.com/iktos/generation-under-synthetic-constraint ). Graphic Abstract In-silico synthesizability Retrosynthesis artificial intelligence machine learning In silico molecular generation Information technology Chemistry Hamza Tajmouati verfasserin aut Vinicius Barros Ribeiro da Silva verfasserin aut Brian Ross Atwood verfasserin aut Robin Fourcade verfasserin aut Yann Gaston-Mathé verfasserin aut Nicolas Do Huu verfasserin aut Quentin Perron verfasserin aut In Journal of Cheminformatics BMC, 2010 15(2023), 1, Seite 17 (DE-627)594779219 (DE-600)2486539-4 17582946 nnns volume:15 year:2023 number:1 pages:17 https://doi.org/10.1186/s13321-023-00742-8 kostenfrei https://doaj.org/article/c00070d8eba741aeb536bbb3910125d4 kostenfrei https://doi.org/10.1186/s13321-023-00742-8 kostenfrei https://doaj.org/toc/1758-2946 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 17
allfields_unstemmed	10.1186/s13321-023-00742-8 doi (DE-627)DOAJ091741920 (DE-599)DOAJc00070d8eba741aeb536bbb3910125d4 DE-627 ger DE-627 rakwb eng T58.5-58.64 QD1-999 Maud Parrot verfasserin aut Integrating synthetic accessibility with AI-based generative drug design 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule “synthesizability”, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic accessibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). We start by comparing several synthetic accessibility scores to a binary “chemist score” as estimated by chemists on a bench of generated molecules, as a first experimental validation that the RScore is a reliable synthetic accessibility score. We then describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables our molecular generators to produce more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on ( https://github.com/iktos/generation-under-synthetic-constraint ). Graphic Abstract In-silico synthesizability Retrosynthesis artificial intelligence machine learning In silico molecular generation Information technology Chemistry Hamza Tajmouati verfasserin aut Vinicius Barros Ribeiro da Silva verfasserin aut Brian Ross Atwood verfasserin aut Robin Fourcade verfasserin aut Yann Gaston-Mathé verfasserin aut Nicolas Do Huu verfasserin aut Quentin Perron verfasserin aut In Journal of Cheminformatics BMC, 2010 15(2023), 1, Seite 17 (DE-627)594779219 (DE-600)2486539-4 17582946 nnns volume:15 year:2023 number:1 pages:17 https://doi.org/10.1186/s13321-023-00742-8 kostenfrei https://doaj.org/article/c00070d8eba741aeb536bbb3910125d4 kostenfrei https://doi.org/10.1186/s13321-023-00742-8 kostenfrei https://doaj.org/toc/1758-2946 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 17
allfieldsGer	10.1186/s13321-023-00742-8 doi (DE-627)DOAJ091741920 (DE-599)DOAJc00070d8eba741aeb536bbb3910125d4 DE-627 ger DE-627 rakwb eng T58.5-58.64 QD1-999 Maud Parrot verfasserin aut Integrating synthetic accessibility with AI-based generative drug design 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule “synthesizability”, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic accessibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). We start by comparing several synthetic accessibility scores to a binary “chemist score” as estimated by chemists on a bench of generated molecules, as a first experimental validation that the RScore is a reliable synthetic accessibility score. We then describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables our molecular generators to produce more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on ( https://github.com/iktos/generation-under-synthetic-constraint ). Graphic Abstract In-silico synthesizability Retrosynthesis artificial intelligence machine learning In silico molecular generation Information technology Chemistry Hamza Tajmouati verfasserin aut Vinicius Barros Ribeiro da Silva verfasserin aut Brian Ross Atwood verfasserin aut Robin Fourcade verfasserin aut Yann Gaston-Mathé verfasserin aut Nicolas Do Huu verfasserin aut Quentin Perron verfasserin aut In Journal of Cheminformatics BMC, 2010 15(2023), 1, Seite 17 (DE-627)594779219 (DE-600)2486539-4 17582946 nnns volume:15 year:2023 number:1 pages:17 https://doi.org/10.1186/s13321-023-00742-8 kostenfrei https://doaj.org/article/c00070d8eba741aeb536bbb3910125d4 kostenfrei https://doi.org/10.1186/s13321-023-00742-8 kostenfrei https://doaj.org/toc/1758-2946 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 17
allfieldsSound	10.1186/s13321-023-00742-8 doi (DE-627)DOAJ091741920 (DE-599)DOAJc00070d8eba741aeb536bbb3910125d4 DE-627 ger DE-627 rakwb eng T58.5-58.64 QD1-999 Maud Parrot verfasserin aut Integrating synthetic accessibility with AI-based generative drug design 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule “synthesizability”, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic accessibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). We start by comparing several synthetic accessibility scores to a binary “chemist score” as estimated by chemists on a bench of generated molecules, as a first experimental validation that the RScore is a reliable synthetic accessibility score. We then describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables our molecular generators to produce more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on ( https://github.com/iktos/generation-under-synthetic-constraint ). Graphic Abstract In-silico synthesizability Retrosynthesis artificial intelligence machine learning In silico molecular generation Information technology Chemistry Hamza Tajmouati verfasserin aut Vinicius Barros Ribeiro da Silva verfasserin aut Brian Ross Atwood verfasserin aut Robin Fourcade verfasserin aut Yann Gaston-Mathé verfasserin aut Nicolas Do Huu verfasserin aut Quentin Perron verfasserin aut In Journal of Cheminformatics BMC, 2010 15(2023), 1, Seite 17 (DE-627)594779219 (DE-600)2486539-4 17582946 nnns volume:15 year:2023 number:1 pages:17 https://doi.org/10.1186/s13321-023-00742-8 kostenfrei https://doaj.org/article/c00070d8eba741aeb536bbb3910125d4 kostenfrei https://doi.org/10.1186/s13321-023-00742-8 kostenfrei https://doaj.org/toc/1758-2946 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 17
language	English
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title_auth	Integrating synthetic accessibility with AI-based generative drug design
abstract	Abstract Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule “synthesizability”, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic accessibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). We start by comparing several synthetic accessibility scores to a binary “chemist score” as estimated by chemists on a bench of generated molecules, as a first experimental validation that the RScore is a reliable synthetic accessibility score. We then describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables our molecular generators to produce more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on ( https://github.com/iktos/generation-under-synthetic-constraint ). Graphic Abstract
abstractGer	Abstract Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule “synthesizability”, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic accessibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). We start by comparing several synthetic accessibility scores to a binary “chemist score” as estimated by chemists on a bench of generated molecules, as a first experimental validation that the RScore is a reliable synthetic accessibility score. We then describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables our molecular generators to produce more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on ( https://github.com/iktos/generation-under-synthetic-constraint ). Graphic Abstract
abstract_unstemmed	Abstract Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule “synthesizability”, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic accessibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). We start by comparing several synthetic accessibility scores to a binary “chemist score” as estimated by chemists on a bench of generated molecules, as a first experimental validation that the RScore is a reliable synthetic accessibility score. We then describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables our molecular generators to produce more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on ( https://github.com/iktos/generation-under-synthetic-constraint ). Graphic Abstract
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url	https://doi.org/10.1186/s13321-023-00742-8 https://doaj.org/article/c00070d8eba741aeb536bbb3910125d4 https://doaj.org/toc/1758-2946
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author2	Hamza Tajmouati Vinicius Barros Ribeiro da Silva Brian Ross Atwood Robin Fourcade Yann Gaston-Mathé Nicolas Do Huu Quentin Perron
author2Str	Hamza Tajmouati Vinicius Barros Ribeiro da Silva Brian Ross Atwood Robin Fourcade Yann Gaston-Mathé Nicolas Do Huu Quentin Perron
ppnlink	594779219
callnumber-subject	T - General Technology
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doi_str	10.1186/s13321-023-00742-8
callnumber-a	T58.5-58.64
up_date	2024-07-03T22:00:55.310Z
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However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule “synthesizability”, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic accessibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). We start by comparing several synthetic accessibility scores to a binary “chemist score” as estimated by chemists on a bench of generated molecules, as a first experimental validation that the RScore is a reliable synthetic accessibility score. We then describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables our molecular generators to produce more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on ( https://github.com/iktos/generation-under-synthetic-constraint ). 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