1
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Zhang Y, Zhang Z, Ke D, Pan X, Wang X, Xiao X, Ji C. FragGrow: A Web Server for Structure-Based Drug Design by Fragment Growing within Constraints. J Chem Inf Model 2024; 64:3970-3976. [PMID: 38725251 DOI: 10.1021/acs.jcim.4c00154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2024]
Abstract
Fragment growing is an important ligand design strategy in drug discovery. In this study, we present FragGrow, a web server that facilitates structure-based drug design by fragment growing. FragGrow offers two working modes: one for growing molecules through the direct replacement of hydrogen atoms or substructures and the other for growing via virtual synthesis. FragGrow works by searching for suitable fragments that meet a set of constraints from an indexed 3D fragment database and using them to create new compounds in 3D space. The users can set a range of constraints when searching for their desired fragment, including the fragment's ability to interact with specific protein sites; its size, topology, and physicochemical properties; and the presence of particular heteroatoms and functional groups within the fragment. We hope that FragGrow will serve as a useful tool for medicinal chemists in ligand design. The FragGrow server is freely available to researchers and can be accessed at https://fraggrow.xundrug.cn.
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Affiliation(s)
- Yueqing Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China
| | - Zhihan Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China
| | - Dongliang Ke
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China
| | - Xiaolin Pan
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China
| | - Xingyu Wang
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China
| | - Xudong Xiao
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China
| | - Changge Ji
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China
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2
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Hönig SMN, Flachsenberg F, Ehrt C, Neumann A, Schmidt R, Lemmen C, Rarey M. SpaceGrow: efficient shape-based virtual screening of billion-sized combinatorial fragment spaces. J Comput Aided Mol Des 2024; 38:13. [PMID: 38493240 PMCID: PMC10944417 DOI: 10.1007/s10822-024-00551-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 02/13/2024] [Indexed: 03/18/2024]
Abstract
The growing size of make-on-demand chemical libraries is posing new challenges to cheminformatics. These ultra-large chemical libraries became too large for exhaustive enumeration. Using a combinatorial approach instead, the resource requirement scales approximately with the number of synthons instead of the number of molecules. This gives access to billions or trillions of compounds as so-called chemical spaces with moderate hardware and in a reasonable time frame. While extremely performant ligand-based 2D methods exist in this context, 3D methods still largely rely on exhaustive enumeration and therefore fail to apply. Here, we present SpaceGrow: a novel shape-based 3D approach for ligand-based virtual screening of billions of compounds within hours on a single CPU. Compared to a conventional superposition tool, SpaceGrow shows comparable pose reproduction capacity based on RMSD and superior ranking performance while being orders of magnitude faster. Result assessment of two differently sized subsets of the eXplore space reveals a higher probability of finding superior results in larger spaces highlighting the potential of searching in ultra-large spaces. Furthermore, the application of SpaceGrow in a drug discovery workflow was investigated in four examples involving G protein-coupled receptors (GPCRs) with the aim to identify compounds with similar binding capabilities and molecular novelty.
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Affiliation(s)
- Sophia M N Hönig
- BioSolveIT, An der Ziegelei 79, 53757, Sankt Augustin, Germany
- Universität Hamburg, ZBH - Center for Bioinformatics, Albert-Einstein-Ring 8-10, 22761, Hamburg, Germany
| | | | - Christiane Ehrt
- Universität Hamburg, ZBH - Center for Bioinformatics, Albert-Einstein-Ring 8-10, 22761, Hamburg, Germany
| | | | - Robert Schmidt
- BioSolveIT, An der Ziegelei 79, 53757, Sankt Augustin, Germany
| | | | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Albert-Einstein-Ring 8-10, 22761, Hamburg, Germany.
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3
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Klarich K, Goldman B, Kramer T, Riley P, Walters WP. Thompson Sampling─An Efficient Method for Searching Ultralarge Synthesis on Demand Databases. J Chem Inf Model 2024; 64:1158-1171. [PMID: 38316125 PMCID: PMC10900287 DOI: 10.1021/acs.jcim.3c01790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
Over the last five years, virtual screening of ultralarge synthesis on-demand libraries has emerged as a powerful tool for hit identification in drug discovery programs. As these libraries have grown to tens of billions of molecules, we have reached a point where it is no longer cost-effective to screen every molecule virtually. To address these challenges, several groups have developed heuristic search methods to rapidly identify the best molecules on a virtual screen. This article describes the application of Thompson sampling (TS), an active learning approach that streamlines the virtual screening of large combinatorial libraries by performing a probabilistic search in the reagent space, thereby never requiring the full enumeration of the library. TS is a general technique that can be applied to various virtual screening modalities, including 2D and 3D similarity search, docking, and application of machine-learning models. In an illustrative example, we show that TS can identify more than half of the top 100 molecules from a docking-based virtual screen of 335 million molecules by evaluating 1% of the data set.
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Affiliation(s)
- Kathryn Klarich
- ReNAgade Therapeutics, 640 Memorial Drive, Cambridge, Massachusetts 02139, United States
| | - Brian Goldman
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02141, United States
| | - Trevor Kramer
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02141, United States
| | - Patrick Riley
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02141, United States
| | - W Patrick Walters
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02141, United States
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4
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Tropsha A, Isayev O, Varnek A, Schneider G, Cherkasov A. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat Rev Drug Discov 2024; 23:141-155. [PMID: 38066301 DOI: 10.1038/s41573-023-00832-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2023] [Indexed: 02/08/2024]
Abstract
Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.
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Affiliation(s)
| | | | | | | | - Artem Cherkasov
- University of British Columbia, Vancouver, BC, Canada.
- Photonic Inc., Coquitlam, BC, Canada.
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5
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Stanley M, Segler M. Fake it until you make it? Generative de novo design and virtual screening of synthesizable molecules. Curr Opin Struct Biol 2023; 82:102658. [PMID: 37473637 DOI: 10.1016/j.sbi.2023.102658] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 07/22/2023]
Abstract
Computational techniques, including virtual screening, de novo design, and generative models, play an increasing role in expediting DMTA cycles for modern molecular discovery. However, computationally proposed molecules must be synthetically feasible for laboratory testing. In this perspective, we offer a succinct introduction to the subject, and showcase typical workflows to integrate synthesis planning, synthesizability scoring, and molecule generation. Finally, we address limitations and opportunities for future research.
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Affiliation(s)
- Megan Stanley
- Microsoft Research AI4Science, UK. https://twitter.com/@megjanestanley
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6
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Ivanenkov Y, Zagribelnyy B, Malyshev A, Evteev S, Terentiev V, Kamya P, Bezrukov D, Aliper A, Ren F, Zhavoronkov A. The Hitchhiker's Guide to Deep Learning Driven Generative Chemistry. ACS Med Chem Lett 2023; 14:901-915. [PMID: 37465301 PMCID: PMC10351082 DOI: 10.1021/acsmedchemlett.3c00041] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 06/09/2023] [Indexed: 07/20/2023] Open
Abstract
This microperspective covers the most recent research outcomes of artificial intelligence (AI) generated molecular structures from the point of view of the medicinal chemist. The main focus is on studies that include synthesis and experimental in vitro validation in biochemical assays of the generated molecular structures, where we analyze the reported structures' relevance in modern medicinal chemistry and their novelty. The authors believe that this review would be appreciated by medicinal chemistry and AI-driven drug design (AIDD) communities and can be adopted as a comprehensive approach for qualifying different research outcomes in AIDD.
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Affiliation(s)
- Yan Ivanenkov
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Bogdan Zagribelnyy
- Insilico
Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, P.O.
Box 145748, Masdar City, Abu Dhabi United Arab Emirates
| | - Alex Malyshev
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Sergei Evteev
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Victor Terentiev
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Petrina Kamya
- Insilico
Medicine Canada Inc., 3710-1250 René-Lévesque Blvd W, Montreal, Quebec, Canada H3B 4W8
| | - Dmitry Bezrukov
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Alex Aliper
- Insilico
Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, P.O.
Box 145748, Masdar City, Abu Dhabi United Arab Emirates
| | - Feng Ren
- Insilico
Medicine Shanghai Ltd., Suite 901, Tower C, Changtai Plaza, 2889 Jinke Road, Pudong New District, Shanghai 201203, China
| | - Alex Zhavoronkov
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
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7
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Korn M, Ehrt C, Ruggiu F, Gastreich M, Rarey M. Navigating large chemical spaces in early-phase drug discovery. Curr Opin Struct Biol 2023; 80:102578. [PMID: 37019067 DOI: 10.1016/j.sbi.2023.102578] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 01/28/2023] [Accepted: 02/26/2023] [Indexed: 04/07/2023]
Abstract
The size of actionable chemical spaces is surging, owing to a variety of novel techniques, both computational and experimental. As a consequence, novel molecular matter is now at our fingertips that cannot and should not be neglected in early-phase drug discovery. Huge, combinatorial, make-on-demand chemical spaces with high probability of synthetic success rise exponentially in content, generative machine learning models go hand in hand with synthesis prediction, and DNA-encoded libraries offer new ways of hit structure discovery. These technologies enable to search for new chemical matter in a much broader and deeper manner with less effort and fewer financial resources. These transformational developments require new cheminformatics approaches to make huge chemical spaces searchable and analyzable with low resources, and with as little energy consumption as possible. Substantial progress has been made in the past years with respect to computation as well as organic synthesis. First examples of bioactive compounds resulting from the successful use of these novel technologies demonstrate their power to contribute to tomorrow's drug discovery programs. This article gives a compact overview of the state-of-the-art.
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Affiliation(s)
- Malte Korn
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstr. 43, 20146 Hamburg, Germany
| | - Christiane Ehrt
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstr. 43, 20146 Hamburg, Germany
| | - Fiorella Ruggiu
- insitro, 279 E Grand Ave., CA 94608, South San Francisco, USA
| | - Marcus Gastreich
- BioSolveIT GmbH, An der Ziegelei 79, 53757 Sankt Augustin, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstr. 43, 20146 Hamburg, Germany.
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8
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Zabolotna Y, Bonachera F, Horvath D, Lin A, Marcou G, Klimchuk O, Varnek A. Chemspace Atlas: Multiscale Chemography of Ultralarge Libraries for Drug Discovery. J Chem Inf Model 2022; 62:4537-4548. [DOI: 10.1021/acs.jcim.2c00509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Yuliana Zabolotna
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Fanny Bonachera
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Dragos Horvath
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Arkadii Lin
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Gilles Marcou
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Olga Klimchuk
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Alexandre Varnek
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
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9
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Akhmetshin T, Lin A, Mazitov D, Zabolotna Y, Ziaikin E, Madzhidov T, Varnek A. HyFactor: A Novel Open-Source, Graph-Based Architecture for Chemical Structure Generation. J Chem Inf Model 2022; 62:3524-3534. [PMID: 35876159 DOI: 10.1021/acs.jcim.2c00744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Graph-based architectures are becoming increasingly popular as a tool for structure generation. Here, we introduce novel open-source architecture HyFactor in which, similar to the InChI linear notation, the number of hydrogens attached to the heavy atoms was considered instead of the bond types. HyFactor was benchmarked on the ZINC 250K, MOSES, and ChEMBL data sets against conventional graph-based architecture ReFactor, representing our implementation of the reported DEFactor architecture in the literature. On average, HyFactor models contain some 20% less fitting parameters than those of ReFactor. The two architectures display similar validity, uniqueness, and reconstruction rates. Compared to the training set compounds, HyFactor generates more similar structures than ReFactor. This could be explained by the fact that the latter generates many open-chain analogues of cyclic structures in the training set. It has been demonstrated that the reconstruction error of heavy molecules can be significantly reduced using the data augmentation technique. The codes of HyFactor and ReFactor as well as all models obtained in this study are publicly available from our GitHub repository: https://github.com/Laboratoire-de-Chemoinformatique/HyFactor.
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Affiliation(s)
- Tagir Akhmetshin
- Laboratory of Chemoinformatics, UMR 7140 CNRS, University of Strasbourg, 4 Blaise Pascal Str., 67081 Strasbourg, France
| | - Arkadii Lin
- Laboratory of Chemoinformatics, UMR 7140 CNRS, University of Strasbourg, 4 Blaise Pascal Str., 67081 Strasbourg, France
| | - Daniyar Mazitov
- Laboratory of Chemoinformatics and Molecular Modeling, Butlerov Institute of Chemistry, Kazan Federal University, 18 Kremlyovskaya Str., 420008 Kazan, Russia
| | - Yuliana Zabolotna
- Laboratory of Chemoinformatics, UMR 7140 CNRS, University of Strasbourg, 4 Blaise Pascal Str., 67081 Strasbourg, France
| | - Evgenii Ziaikin
- Laboratory of Chemoinformatics and Molecular Modeling, Butlerov Institute of Chemistry, Kazan Federal University, 18 Kremlyovskaya Str., 420008 Kazan, Russia
| | - Timur Madzhidov
- Laboratory of Chemoinformatics and Molecular Modeling, Butlerov Institute of Chemistry, Kazan Federal University, 18 Kremlyovskaya Str., 420008 Kazan, Russia
| | - Alexandre Varnek
- Laboratory of Chemoinformatics, UMR 7140 CNRS, University of Strasbourg, 4 Blaise Pascal Str., 67081 Strasbourg, France
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10
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Rarey M, Nicklaus MC, Warr W. Special Issue on Reaction Informatics and Chemical Space. J Chem Inf Model 2022; 62:2009-2010. [DOI: 10.1021/acs.jcim.2c00390] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Matthias Rarey
- Universität Hamburg, ZBH − Center for Bioinformatics, 20146 Hamburg, Germany
| | - Marc C. Nicklaus
- NCI, NIH, CADD Group, NCI-Frederick, Frederick, Maryland 21702, United States
| | - Wendy Warr
- Wendy Warr & Associates, Cheshire CW4 7HZ, U.K
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11
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Abstract
![]()
One application area
of computational methods in drug discovery
is the automated design of small molecules. Despite the large number
of publications describing methods and their application in both retrospective
and prospective studies, there is a lack of agreement on terminology
and key attributes to distinguish these various systems. We introduce
Automated Chemical Design (ACD) Levels to clearly define the level
of autonomy along the axes of ideation and decision making. To fully
illustrate this framework, we provide literature exemplars and place
some notable methods and applications into the levels. The ACD framework
provides a common language for describing automated small molecule
design systems and enables medicinal chemists to better understand
and evaluate such systems.
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Affiliation(s)
- Brian Goldman
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02139, United States
| | - Steven Kearnes
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02139, United States
| | - Trevor Kramer
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02139, United States
| | - Patrick Riley
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02139, United States
| | - W Patrick Walters
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02139, United States
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12
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Wahl J, Sander T. Fully Automated Creation of Virtual Chemical Fragment Spaces Using the Open-Source Library OpenChemLib. J Chem Inf Model 2022; 62:2202-2211. [DOI: 10.1021/acs.jcim.1c01041] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Joel Wahl
- Scientific Computing Drug Discovery, Idorsia Pharmaceuticals Ltd., Hegenheimermattweg 91, CH-4123 Allschwil, Switzerland
| | - Thomas Sander
- Scientific Computing Drug Discovery, Idorsia Pharmaceuticals Ltd., Hegenheimermattweg 91, CH-4123 Allschwil, Switzerland
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13
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Zabolotna Y, Volochnyuk DM, Ryabukhin SV, Horvath D, Gavrilenko KS, Marcou G, Moroz YS, Oksiuta O, Varnek A. A Close-up Look at the Chemical Space of Commercially Available Building Blocks for Medicinal Chemistry. J Chem Inf Model 2021; 62:2171-2185. [PMID: 34928600 DOI: 10.1021/acs.jcim.1c00811] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The ability to efficiently synthesize desired compounds can be a limiting factor for chemical space exploration in drug discovery. This ability is conditioned not only by the existence of well-studied synthetic protocols but also by the availability of corresponding reagents, so-called building blocks (BBs). In this work, we present a detailed analysis of the chemical space of 400 000 purchasable BBs. The chemical space was defined by corresponding synthons─fragments contributed to the final molecules upon reaction. They allow an analysis of BB physicochemical properties and diversity, unbiased by the leaving and protective groups in actual reagents. The main classes of BBs were analyzed in terms of their availability, rule-of-two-defined quality, and diversity. Available BBs were eventually compared to a reference set of biologically relevant synthons derived from ChEMBL fragmentation, in order to illustrate how well they cover the actual medicinal chemistry needs. This was performed on a newly constructed universal generative topographic map of synthon chemical space that enables visualization of both libraries and analysis of their overlapped and library-specific regions.
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Affiliation(s)
- Yuliana Zabolotna
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Dmitriy M Volochnyuk
- Institute of Organic Chemistry, National Academy of Sciences of Ukraine, Murmanska Street 5, Kyiv 02660, Ukraine.,Enamine Ltd., 78 Chervonotkatska str., 02660 Kiev, Ukraine
| | - Sergey V Ryabukhin
- The Institute of High Technologies, Kyiv National Taras Shevchenko University, 64 Volodymyrska Street, Kyiv 01601, Ukraine.,Enamine Ltd., 78 Chervonotkatska str., 02660 Kiev, Ukraine
| | - Dragos Horvath
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Konstantin S Gavrilenko
- Research-And-Education ChemBioCenter, National Taras Shevchenko University of Kyiv, Chervonotkatska str., 61, 03022 Kiev, Ukraine.,Enamine Ltd., 78 Chervonotkatska str., 02660 Kiev, Ukraine
| | - Gilles Marcou
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Yurii S Moroz
- Research-And-Education ChemBioCenter, National Taras Shevchenko University of Kyiv, Chervonotkatska str., 61, 03022 Kiev, Ukraine.,Chemspace, Chervonotkatska Street 78, 02094 Kyiv, Ukraine
| | - Oleksandr Oksiuta
- Institute of Organic Chemistry, National Academy of Sciences of Ukraine, Murmanska Street 5, Kyiv 02660, Ukraine.,Chemspace, Chervonotkatska Street 78, 02094 Kyiv, Ukraine
| | - Alexandre Varnek
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France.,Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, 001-0021 Sapporo, Japan
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