Gian Marco Ghiandoni, Stuart R Flanagan, Michael J Bodkin, Maria Giulia Nizi, Albert Galera-Prat, Annalaura Brai, Beining Chen, James E A Wallace, Dimitar Hristozov, James Webster, Giuseppe Manfroni, Lari Lehtiö, Oriana Tabarrini, Valerie J Gillet
{"title":"使用反应载体进行可合成的从头设计:应用于 PARP1 抑制剂。","authors":"Gian Marco Ghiandoni, Stuart R Flanagan, Michael J Bodkin, Maria Giulia Nizi, Albert Galera-Prat, Annalaura Brai, Beining Chen, James E A Wallace, Dimitar Hristozov, James Webster, Giuseppe Manfroni, Lari Lehtiö, Oriana Tabarrini, Valerie J Gillet","doi":"10.1002/minf.202300183","DOIUrl":null,"url":null,"abstract":"<p><p>De novo design has been a hotly pursued topic for many years. Most recent developments have involved the use of deep learning methods for generative molecular design. Despite increasing levels of algorithmic sophistication, the design of molecules that are synthetically accessible remains a major challenge. Reaction-based de novo design takes a conceptually simpler approach and aims to address synthesisability directly by mimicking synthetic chemistry and driving structural transformations by known reactions that are applied in a stepwise manner. However, the use of a small number of hand-coded transformations restricts the chemical space that can be accessed and there are few examples in the literature where molecules and their synthetic routes have been designed and executed successfully. Here we describe the application of reaction-based de novo design to the design of synthetically accessible and biologically active compounds as proof-of-concept of our reaction vector-based software. Reaction vectors are derived automatically from known reactions and allow access to a wide region of synthetically accessible chemical space. The design was aimed at producing molecules that are active against PARP1 and which have improved brain penetration properties compared to existing PARP1 inhibitors. We synthesised a selection of the designed molecules according to the provided synthetic routes and tested them experimentally. The results demonstrate that reaction vectors can be applied to the design of novel molecules of biological relevance that are also synthetically accessible.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":" ","pages":"e202300183"},"PeriodicalIF":2.8000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11475289/pdf/","citationCount":"0","resultStr":"{\"title\":\"Synthetically accessible de novo design using reaction vectors: Application to PARP1 inhibitors.\",\"authors\":\"Gian Marco Ghiandoni, Stuart R Flanagan, Michael J Bodkin, Maria Giulia Nizi, Albert Galera-Prat, Annalaura Brai, Beining Chen, James E A Wallace, Dimitar Hristozov, James Webster, Giuseppe Manfroni, Lari Lehtiö, Oriana Tabarrini, Valerie J Gillet\",\"doi\":\"10.1002/minf.202300183\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>De novo design has been a hotly pursued topic for many years. 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Reaction vectors are derived automatically from known reactions and allow access to a wide region of synthetically accessible chemical space. The design was aimed at producing molecules that are active against PARP1 and which have improved brain penetration properties compared to existing PARP1 inhibitors. We synthesised a selection of the designed molecules according to the provided synthetic routes and tested them experimentally. 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Synthetically accessible de novo design using reaction vectors: Application to PARP1 inhibitors.
De novo design has been a hotly pursued topic for many years. Most recent developments have involved the use of deep learning methods for generative molecular design. Despite increasing levels of algorithmic sophistication, the design of molecules that are synthetically accessible remains a major challenge. Reaction-based de novo design takes a conceptually simpler approach and aims to address synthesisability directly by mimicking synthetic chemistry and driving structural transformations by known reactions that are applied in a stepwise manner. However, the use of a small number of hand-coded transformations restricts the chemical space that can be accessed and there are few examples in the literature where molecules and their synthetic routes have been designed and executed successfully. Here we describe the application of reaction-based de novo design to the design of synthetically accessible and biologically active compounds as proof-of-concept of our reaction vector-based software. Reaction vectors are derived automatically from known reactions and allow access to a wide region of synthetically accessible chemical space. The design was aimed at producing molecules that are active against PARP1 and which have improved brain penetration properties compared to existing PARP1 inhibitors. We synthesised a selection of the designed molecules according to the provided synthetic routes and tested them experimentally. The results demonstrate that reaction vectors can be applied to the design of novel molecules of biological relevance that are also synthetically accessible.
期刊介绍:
Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics succeeded QSAR & Combinatorial Science in 2010.
Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation.
The journal''s scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules. As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.