1
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Lübbers J, Lessel U, Rarey M. Enhanced Calculation of Property Distributions in Chemical Fragment Spaces. J Chem Inf Model 2024; 64:2008-2020. [PMID: 38466793 PMCID: PMC10966640 DOI: 10.1021/acs.jcim.4c00147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 02/19/2024] [Accepted: 02/22/2024] [Indexed: 03/13/2024]
Abstract
Chemical fragment spaces exceed traditional virtual compound libraries by orders of magnitude, making them ideal search spaces for drug design projects. However, due to their immense size, they are not compatible with traditional analysis and search algorithms that rely on the enumeration of molecules. In this paper, we present SpaceProp2, an evolution of the SpaceProp algorithm, which enables the calculation of exact property distributions for chemical fragment spaces without enumerating them. We extend the original algorithm by the capabilities to compute distributions for the TPSA, the number of rotatable bonds, and the occurrence of user-defined molecular structures in the form of SMARTS patterns. Furthermore, SpaceProp2 produces example molecules for every property bin, enabling a detailed interpretation of the distributions. We demonstrate SpaceProp2 on six established make-on-demand chemical fragment spaces as well as BICLAIM, the in-house fragment space of Boehringer Ingelheim. The possibility to search multiple SMARTS patterns simultaneously as well as the produced example molecules offers previously impossible insights into the composition of these vast combinatorial molecule collections, making it an ideal tool for the analysis and design of chemical fragment spaces.
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Affiliation(s)
- Justin Lübbers
- ZBH
- Center for Bioinformatics, Research Group for Computational Molecular
Design, Universität Hamburg, Hamburg 22761, Germany
| | - Uta Lessel
- Computational
Chemistry, Boehringer Ingelheim Pharma GmbH
& Co. KG, Biberach
an der Riss 88437, Germany
| | - Matthias Rarey
- ZBH
- Center for Bioinformatics, Research Group for Computational Molecular
Design, Universität Hamburg, Hamburg 22761, Germany
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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] [Received: 11/06/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [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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Zhang R, Mahjour B, Outlaw A, McGrath A, Hopper T, Kelley B, Walters WP, Cernak T. Exploring the combinatorial explosion of amine-acid reaction space via graph editing. Commun Chem 2024; 7:22. [PMID: 38310120 PMCID: PMC10838272 DOI: 10.1038/s42004-024-01101-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 01/08/2024] [Indexed: 02/05/2024] Open
Abstract
Amines and carboxylic acids are abundant chemical feedstocks that are nearly exclusively united via the amide coupling reaction. The disproportionate use of the amide coupling leaves a large section of unexplored reaction space between amines and acids: two of the most common chemical building blocks. Herein we conduct a thorough exploration of amine-acid reaction space via systematic enumeration of reactions involving a simple amine-carboxylic acid pair. This approach to chemical space exploration investigates the coarse and fine modulation of physicochemical properties and molecular shapes. With the invention of reaction methods becoming increasingly automated and bringing conceptual reactions into reality, our map provides an entirely new axis of chemical space exploration for rational property design.
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Affiliation(s)
- Rui Zhang
- Department of Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - Babak Mahjour
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - Andrew Outlaw
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - Andrew McGrath
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI, USA
| | | | | | | | - Tim Cernak
- Department of Chemistry, University of Michigan, Ann Arbor, MI, USA.
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI, USA.
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5
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Melancon K, Pliushcheuskaya P, Meiler J, Künze G. Targeting ion channels with ultra-large library screening for hit discovery. Front Mol Neurosci 2024; 16:1336004. [PMID: 38249296 PMCID: PMC10796734 DOI: 10.3389/fnmol.2023.1336004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/05/2023] [Indexed: 01/23/2024] Open
Abstract
Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.
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Affiliation(s)
- Kortney Melancon
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | | | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
- Medical Faculty, Institute for Drug Discovery, Leipzig University, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, Leipzig, Germany
| | - Georg Künze
- Medical Faculty, Institute for Drug Discovery, Leipzig University, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, Leipzig University, Leipzig, Germany
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6
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Ginex T, Madruga E, Martinez A, Gil C. MBC and ECBL libraries: outstanding tools for drug discovery. Front Pharmacol 2023; 14:1244317. [PMID: 37637414 PMCID: PMC10457160 DOI: 10.3389/fphar.2023.1244317] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 08/04/2023] [Indexed: 08/29/2023] Open
Abstract
Chemical libraries have become of utmost importance to boost drug discovery processes. It is widely accepted that the quality of a chemical library depends, among others, on its availability and chemical diversity which help in rising the chances of finding good hits. In this regard, our group has developed a source for useful chemicals named Medicinal and Biological Chemistry (MBC) library. It originates from more than 30 years of experience in drug design and discovery of our research group and has successfully provided effective hits for neurological, neurodegenerative and infectious diseases. Moreover, in the last years, the European research infrastructure for chemical biology EU-OPENSCREEN has generated the European Chemical Biology library (ECBL) to be used as a source of hits for drug discovery. Here we present and discuss the updated version of the MBC library (MBC v.2022), enriched with new scaffolds and containing more than 2,500 compounds together with ECBL that collects about 100,000 small molecules. To properly address the improved potentialities of the new version of our MBC library in drug discovery, up to 44 among physicochemical and pharmaceutical properties have been calculated and compared with those of other well-known publicly available libraries. For comparison, we have used ZINC20, DrugBank, ChEMBL library, ECBL and NuBBE along with an approved drug library. Final results allowed to confirm the competitive chemical space covered by MBC v.2022 and ECBL together with suitable drug-like properties. In all, we can affirm that these two libraries represent an interesting source of new hits for drug discovery.
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Affiliation(s)
- Tiziana Ginex
- Centro de Investigaciones Biológicas “Margarita Salas” (CIB-CSIC), Madrid, Spain
| | - Enrique Madruga
- Centro de Investigaciones Biológicas “Margarita Salas” (CIB-CSIC), Madrid, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Ana Martinez
- Centro de Investigaciones Biológicas “Margarita Salas” (CIB-CSIC), Madrid, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Carmen Gil
- Centro de Investigaciones Biológicas “Margarita Salas” (CIB-CSIC), Madrid, Spain
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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: 9] [Impact Index Per Article: 9.0] [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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Sadybekov AV, Katritch V. Computational approaches streamlining drug discovery. Nature 2023; 616:673-685. [PMID: 37100941 DOI: 10.1038/s41586-023-05905-z] [Citation(s) in RCA: 151] [Impact Index Per Article: 151.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 03/01/2023] [Indexed: 04/28/2023]
Abstract
Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.
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Affiliation(s)
- Anastasiia V Sadybekov
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
- Center for New Technologies in Drug Discovery and Development, Bridge Institute, Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA
| | - Vsevolod Katritch
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Center for New Technologies in Drug Discovery and Development, Bridge Institute, Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA.
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9
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Dang HT, Nguyen VD, Haug GC, Arman HD, Larionov OV. Decarboxylative Triazolation Enables Direct Construction of Triazoles from Carboxylic Acids. JACS AU 2023; 3:813-822. [PMID: 37006773 PMCID: PMC10052276 DOI: 10.1021/jacsau.2c00606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 06/19/2023]
Abstract
Triazoles have major roles in chemistry, medicine, and materials science, as centrally important heterocyclic motifs and bioisosteric replacements for amides, carboxylic acids, and other carbonyl groups, as well as some of the most widely used linkers in click chemistry. Yet, the chemical space and molecular diversity of triazoles remains limited by the accessibility of synthetically challenging organoazides, thereby requiring preinstallation of the azide precursors and restricting triazole applications. We report herein a photocatalytic, tricomponent decarboxylative triazolation reaction that for the first time enables direct conversion of carboxylic acids to triazoles in a single-step, triple catalytic coupling with alkynes and a simple azide reagent. Data-guided inquiry of the accessible chemical space of decarboxylative triazolation indicates that the transformation can improve access to the structural diversity and molecular complexity of triazoles. Experimental studies demonstrate a broad scope of the synthetic method that includes a variety of carboxylic acid, polymer, and peptide substrates. When performed in the absence of alkynes, the reaction can also be used to access organoazides, thereby obviating preactivation and specialized azide reagents and providing a two-pronged approach to C-N bond-forming decarboxylative functional group interconversions.
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10
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Lv C, Zhao R, Wang X, Liu D, Muschin T, Sun Z, Bai C, Bao A, Bao YS. Copper-Catalyzed Transamidation of Unactivated Secondary Amides via C-H and C-N Bond Simultaneous Activations. J Org Chem 2023; 88:2140-2157. [PMID: 36701175 DOI: 10.1021/acs.joc.2c02551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Here, we demonstrate that α-C-H and C-N bonds of unactivated secondary amides can be activated simultaneously by the copper catalyst to synthesize α-ketoamides or α-ketoesters in one step, which is a challenging and underdeveloped transformation. Using copper as a catalyst and air as an oxidant, the reaction is compatible with a broad range of acetoamides, amines, and alcohols. The preliminary mechanism studies and density functional theory calculation indicated that the reaction process may undergo first radical α-oxygenation and then transamidation with the help of the resonant six-membered N,O-chelation and molecular oxygen plays a role as an initiator to trigger the transamidation process. The combination of chelation assistance and dioxygen selective oxygenation strategy would substantially extend the modern mild synthetic amide cleavage toolbox, and we envision that this broadly applicable method will be of great interest in the biopharmaceutical industry, synthetic chemistry, and agrochemical industry.
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Affiliation(s)
- Cong Lv
- Inner Mongolia Key Laboratory of Green Catalysis, College of Chemistry and Environmental Science, Inner Mongolia Normal University, Hohhot 010022, China
| | - Ruisheng Zhao
- Inner Mongolia Key Laboratory of Green Catalysis, College of Chemistry and Environmental Science, Inner Mongolia Normal University, Hohhot 010022, China
| | - Xiuying Wang
- Inner Mongolia Autonomous Region Animal Epidemic Prevention Center, Hohhot 010020, China
| | - Dan Liu
- Inner Mongolia Key Laboratory of Green Catalysis, College of Chemistry and Environmental Science, Inner Mongolia Normal University, Hohhot 010022, China
| | - Tegshi Muschin
- Inner Mongolia Key Laboratory of Green Catalysis, College of Chemistry and Environmental Science, Inner Mongolia Normal University, Hohhot 010022, China
| | - Zhaorigetu Sun
- College of Chemistry and Chemical Engineering, Inner Mongolia University, Hohhot 010020, China
| | - Chaolumen Bai
- Inner Mongolia Key Laboratory of Green Catalysis, College of Chemistry and Environmental Science, Inner Mongolia Normal University, Hohhot 010022, China
| | - Agula Bao
- Inner Mongolia Key Laboratory of Green Catalysis, College of Chemistry and Environmental Science, Inner Mongolia Normal University, Hohhot 010022, China
| | - Yong-Sheng Bao
- Inner Mongolia Key Laboratory of Green Catalysis, College of Chemistry and Environmental Science, Inner Mongolia Normal University, Hohhot 010022, China
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11
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McMillan AE, Wu WWX, Nichols PL, Wanner BM, Bode JW. A vending machine for drug-like molecules - automated synthesis of virtual screening hits. Chem Sci 2022; 13:14292-14299. [PMID: 36545137 PMCID: PMC9749103 DOI: 10.1039/d2sc05182f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 10/27/2022] [Indexed: 12/24/2022] Open
Abstract
As a result of high false positive rates in virtual screening campaigns, prospective hits must be synthesised for validation. When done manually, this is a time consuming and laborious process. Large "on-demand" virtual libraries (>7 × 1012 members), suitable for preparation using capsule-based automated synthesis and commercial building blocks, were evaluated to determine their structural novelty. One sub-library, constructed from iSnAP capsules, aldehydes and amines, contains unique scaffolds with drug-like physicochemical properties. Virtual screening hits from this iSnAP library were prepared in an automated fashion for evaluation against Aedes aegypti and Phytophthora infestans. In comparison to manual workflows, this approach provided a 10-fold improvement in user efficiency. A streamlined method of relative stereochemical assignment was also devised to augment the rapid synthesis. User efficiency was further improved to 100-fold by downscaling and parallelising capsule-based chemistry on 96-well plates equipped with filter bases. This work demonstrates that automated synthesis consoles can enable the rapid and reliable preparation of attractive virtual screening hits from large virtual libraries.
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Affiliation(s)
- Angus E. McMillan
- Laboratory for Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH ZürichZürich 8093Switzerland
| | - Wilson W. X. Wu
- Laboratory for Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH ZürichZürich 8093Switzerland
| | - Paula L. Nichols
- Laboratory for Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH ZürichZürich 8093Switzerland,Synple Chem AGKemptpark 18Kemptthal 8310Switzerland
| | | | - Jeffrey W. Bode
- Laboratory for Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH ZürichZürich 8093Switzerland
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12
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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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13
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Petrović D, Scott JS, Bodnarchuk MS, Lorthioir O, Boyd S, Hughes GM, Lane J, Wu A, Hargreaves D, Robinson J, Sadowski J. Virtual Screening in the Cloud Identifies Potent and Selective ROS1 Kinase Inhibitors. J Chem Inf Model 2022; 62:3832-3843. [PMID: 35920716 DOI: 10.1021/acs.jcim.2c00644] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
ROS1 rearrangements account for 1-2% of non-small cell lung cancer patients, yet there are no specifically designed, selective ROS1 therapies in the clinic. Previous knowledge of potent ROS1 inhibitors with selectivity over TrkA, a selected antitarget, enabled virtual screening as a hit finding approach in this project. The ligand-based virtual screening was focused on identifying molecules with a similar 3D shape and pharmacophore to the known actives. To that end, we turned to the AstraZeneca virtual library, estimated to cover 1015 synthesizable make-on-demand molecules. We used cloud computing-enabled FastROCS technology to search the enumerated 1010 subset of the full virtual space. A small number of specific libraries were prioritized based on the compound properties and a medicinal chemistry assessment and further enumerated with available building blocks. Following the docking evaluation to the ROS1 structure, the most promising hits were synthesized and tested, resulting in the identification of several potent and selective series. The best among them gave a nanomolar ROS1 inhibitor with over 1000-fold selectivity over TrkA and, from the preliminary established SAR, these have the potential to be further optimized. Our prospective study describes how conceptually simple shape-matching approaches can identify potent and selective compounds by searching ultralarge virtual libraries, demonstrating the applicability of such workflows and their importance in early drug discovery.
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Affiliation(s)
- Dušan Petrović
- Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg 431 50, Sweden
| | - James S Scott
- Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | | | | | - Scott Boyd
- Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - George M Hughes
- Discovery Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, United Kingdom
| | - Jordan Lane
- Discovery Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, United Kingdom
| | - Allan Wu
- Mechanistic and Structural Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, Massachusetts 02451, United States
| | - David Hargreaves
- Mechanistic and Structural Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, United Kingdom
| | - James Robinson
- Mechanistic and Structural Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, United Kingdom
| | - Jens Sadowski
- Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg 431 50, Sweden
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14
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Zahoránszky-Kőhalmi G, Lysov N, Vorontcov I, Wang J, Soundararajan J, Metaxotos D, Mathew B, Sarosh R, Michael SG, Godfrey AG. Algorithm for the Pruning of Synthesis Graphs. J Chem Inf Model 2022; 62:2226-2238. [PMID: 35438992 PMCID: PMC9093600 DOI: 10.1021/acs.jcim.1c01202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Synthesis route planning is in the core of chemical intelligence that will power the autonomous chemistry platforms. In this task, we rely on algorithms to generate possible synthesis routes with the help of retro- and forward-synthetic approaches. Generated synthesis routes can be merged into a synthesis graph which represents theoretical pathways to the target molecule. However, it is often required to modify a synthesis graph due to typical constraints. These constraints might include "undesirable substances", e.g., an intermediate that the chemist does not favor or substances that might be toxic. Consequently, we need to prune the synthesis graph by the elimination of such undesirable substances. Synthesis graphs can be represented as directed (not necessarily acyclic) bipartite graphs, and the pruning of such graphs in the light of a set of undesirable substances has been an open question. In this study, we present the Synthesis Graph Pruning (SGP) algorithm that addresses this question. The input to the SGP algorithm is a synthesis graph and a set of undesirable substances. Furthermore, information for substances is provided as metadata regarding their availability from the inventory. The SGP algorithm operates with a simple local rule set, in order to determine which nodes and edges need to be eliminated from the synthesis graph. In this study, we present the SGP algorithm in detail and provide several case studies that demonstrate the operation of the SGP algorithm. We believe that the SGP algorithm will be an essential component of computer aided synthesis planning.
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Affiliation(s)
| | - Nikita Lysov
- National Center for Advancing Translational Sciences, Rockville, Maryland 20850, United States
| | - Ilia Vorontcov
- National Center for Advancing Translational Sciences, Rockville, Maryland 20850, United States
| | - Jeffrey Wang
- National Center for Advancing Translational Sciences, Rockville, Maryland 20850, United States
| | - Jeyaraman Soundararajan
- National Center for Advancing Translational Sciences, Rockville, Maryland 20850, United States
| | - Dimitrios Metaxotos
- National Center for Advancing Translational Sciences, Rockville, Maryland 20850, United States
| | - Biju Mathew
- National Center for Advancing Translational Sciences, Rockville, Maryland 20850, United States
| | - Rafat Sarosh
- National Center for Advancing Translational Sciences, Rockville, Maryland 20850, United States
| | - Samuel G Michael
- National Center for Advancing Translational Sciences, Rockville, Maryland 20850, United States
| | - Alexander G Godfrey
- National Center for Advancing Translational Sciences, Rockville, Maryland 20850, United States
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15
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Warr WA, Nicklaus MC, Nicolaou CA, Rarey M. Exploration of Ultralarge Compound Collections for Drug Discovery. J Chem Inf Model 2022; 62:2021-2034. [PMID: 35421301 DOI: 10.1021/acs.jcim.2c00224] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Designing new medicines more cheaply and quickly is tightly linked to the quest of exploring chemical space more widely and efficiently. Chemical space is monumentally large, but recent advances in computer software and hardware have enabled researchers to navigate virtual chemical spaces containing billions of chemical structures. This review specifically concerns collections of many millions or even billions of enumerated chemical structures as well as even larger chemical spaces that are not fully enumerated. We present examples of chemical libraries and spaces and the means used to construct them, and we discuss new technologies for searching huge libraries and for searching combinatorially in chemical space. We also cover space navigation techniques and consider new approaches to de novo drug design and the impact of the "autonomous laboratory" on synthesis of designed compounds. Finally, we summarize some other challenges and opportunities for the future.
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Affiliation(s)
- Wendy A Warr
- Wendy Warr & Associates, 6 Berwick Court, Holmes Chapel, Crewe, Cheshire CW4 7HZ, United Kingdom
| | - Marc C Nicklaus
- NCI, NIH, CADD Group, NCI-Frederick, Frederick, Maryland 21702, United States
| | - Christos A Nicolaou
- Discovery Chemistry, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Matthias Rarey
- Universität Hamburg, ZBH Center for Bioinformatics, 20146 Hamburg, Germany
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16
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Hua Y, Fang X, Xing G, Xu Y, Liang L, Deng C, Dai X, Liu H, Lu T, Zhang Y, Chen Y. Effective Reaction-Based De Novo Strategy for Kinase Targets: A Case Study on MERTK Inhibitors. J Chem Inf Model 2022; 62:1654-1668. [PMID: 35353505 DOI: 10.1021/acs.jcim.2c00068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Reaction-based de novo design is the computational generation of novel molecular structures by linking building blocks using reaction vectors derived from chemistry knowledge. In this work, we first adopted a recurrent neural network (RNN) model to generate three groups of building blocks with different functional groups and then constructed an in silico target-focused combinatorial library based on chemical reaction rules. Mer tyrosine kinase (MERTK) was used as a study case. Combined with a scaffold enrichment analysis, 15 novel MERTK inhibitors covering four scaffolds were achieved. Among them, compound 5a obtained an IC50 value of 53.4 nM against MERTK without any further optimization. The efficiency of hit identification could be significantly improved by shrinking the compound library with the fragment iterative optimization strategy and enriching the dominant scaffold in the hinge region. We hope that this strategy can provide new insights for accelerating the drug discovery process.
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Affiliation(s)
- Yi Hua
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Xiaobao Fang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Guomeng Xing
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Yuan Xu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Li Liang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Chenglong Deng
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Xiaowen Dai
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Tao Lu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China.,State Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
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17
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18
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Suay-García B, Bueso-Bordils JI, Falcó A, Antón-Fos GM, Alemán-López PA. Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design. Int J Mol Sci 2022; 23:ijms23031620. [PMID: 35163543 PMCID: PMC8836228 DOI: 10.3390/ijms23031620] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/24/2022] [Accepted: 01/28/2022] [Indexed: 02/04/2023] Open
Abstract
Traditionally, drug development involved the individual synthesis and biological evaluation of hundreds to thousands of compounds with the intention of highlighting their biological activity, selectivity, and bioavailability, as well as their low toxicity. On average, this process of new drug development involved, in addition to high economic costs, a period of several years before hopefully finding a drug with suitable characteristics to drive its commercialization. Therefore, the chemical synthesis of new compounds became the limiting step in the process of searching for or optimizing leads for new drug development. This need for large chemical libraries led to the birth of high-throughput synthesis methods and combinatorial chemistry. Virtual combinatorial chemistry is based on the same principle as real chemistry—many different compounds can be generated from a few building blocks at once. The difference lies in its speed, as millions of compounds can be produced in a few seconds. On the other hand, many virtual screening methods, such as QSAR (Quantitative Sturcture-Activity Relationship), pharmacophore models, and molecular docking, have been developed to study these libraries. These models allow for the selection of molecules to be synthesized and tested with a high probability of success. The virtual combinatorial chemistry–virtual screening tandem has become a fundamental tool in the process of searching for and developing a drug, as it allows the process to be accelerated with extraordinary economic savings.
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Affiliation(s)
- Beatriz Suay-García
- ESI International @ UCHCEU, Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera—CEU, CEU Universities San Bartolomé 55, Alfara del Patriarca, 46115 Valencia, Spain;
- Correspondence:
| | - Jose I. Bueso-Bordils
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
| | - Antonio Falcó
- ESI International @ UCHCEU, Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera—CEU, CEU Universities San Bartolomé 55, Alfara del Patriarca, 46115 Valencia, Spain;
| | - Gerardo M. Antón-Fos
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
| | - Pedro A. Alemán-López
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
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19
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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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20
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Dzobo K. The Role of Natural Products as Sources of Therapeutic Agents for Innovative Drug Discovery. COMPREHENSIVE PHARMACOLOGY 2022. [PMCID: PMC8016209 DOI: 10.1016/b978-0-12-820472-6.00041-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Emerging threats to human health require a concerted effort in search of both preventive and treatment strategies, placing natural products at the center of efforts to obtain new therapies and reduce disease spread and associated mortality. The therapeutic value of compounds found in plants has been known for ages, resulting in their utilization in homes and in clinics for the treatment of many ailments ranging from common headache to serious conditions such as wounds. Despite the advancement observed in the world, plant based medicines are still being used to treat many pathological conditions or are used as alternatives to modern medicines. In most cases, these natural products or plant-based medicines are used in an un-purified state as extracts. A lot of research is underway to identify and purify the active compounds responsible for the healing process. Some of the current drugs used in clinics have their origins as natural products or came from plant extracts. In addition, several synthetic analogues are natural product-based or plant-based. With the emergence of novel infectious agents such as the SARS-CoV-2 in addition to already burdensome diseases such as diabetes, cancer, tuberculosis and HIV/AIDS, there is need to come up with new drugs that can cure these conditions. Natural products offer an opportunity to discover new compounds that can be converted into drugs given their chemical structure diversity. Advances in analytical processes make drug discovery a multi-dimensional process involving computational designing and testing and eventual laboratory screening of potential drug candidates. Lead compounds will then be evaluated for safety, pharmacokinetics and efficacy. New technologies including Artificial Intelligence, better organ and tissue models such as organoids allow virtual screening, automation and high-throughput screening to be part of drug discovery. The use of bioinformatics and computation means that drug discovery can be a fast and efficient process and enable the use of natural products structures to obtain novel drugs. The removal of potential bottlenecks resulting in minimal false positive leads in drug development has enabled an efficient system of drug discovery. This review describes the biosynthesis and screening of natural products during drug discovery as well as methods used in studying natural products.
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21
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Kralj S, Jukič M, Bren U. Commercial SARS-CoV-2 Targeted, Protease Inhibitor Focused and Protein-Protein Interaction Inhibitor Focused Molecular Libraries for Virtual Screening and Drug Design. Int J Mol Sci 2021; 23:393. [PMID: 35008818 PMCID: PMC8745317 DOI: 10.3390/ijms23010393] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/23/2021] [Accepted: 12/28/2021] [Indexed: 01/08/2023] Open
Abstract
Since December 2019, the new SARS-CoV-2-related COVID-19 disease has caused a global pandemic and shut down the public life worldwide. Several proteins have emerged as potential therapeutic targets for drug development, and we sought out to review the commercially available and marketed SARS-CoV-2-targeted libraries ready for high-throughput virtual screening (HTVS). We evaluated the SARS-CoV-2-targeted, protease-inhibitor-focused and protein-protein-interaction-inhibitor-focused libraries to gain a better understanding of how these libraries were designed. The most common were ligand- and structure-based approaches, along with various filtering steps, using molecular descriptors. Often, these methods were combined to obtain the final library. We recognized the abundance of targeted libraries offered and complimented by the inclusion of analytical data; however, serious concerns had to be raised. Namely, vendors lack the information on the library design and the references to the primary literature. Few references to active compounds were also provided when using the ligand-based design and usually only protein classes or a general panel of targets were listed, along with a general reference to the methods, such as molecular docking for the structure-based design. No receptor data, docking protocols or even references to the applied molecular docking software (or other HTVS software), and no pharmacophore or filter design details were given. No detailed functional group or chemical space analyses were reported, and no specific orientation of the libraries toward the design of covalent or noncovalent inhibitors could be observed. All libraries contained pan-assay interference compounds (PAINS), rapid elimination of swill compounds (REOS) and aggregators, as well as focused on the drug-like model, with the majority of compounds possessing their molecular mass around 500 g/mol. These facts do not bode well for the use of the reviewed libraries in drug design and lend themselves to commercial drug companies to focus on and improve.
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Affiliation(s)
- Sebastjan Kralj
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia; (S.K.); (M.J.)
| | - Marko Jukič
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia; (S.K.); (M.J.)
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000 Koper, Slovenia
| | - Urban Bren
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia; (S.K.); (M.J.)
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, SI-6000 Koper, Slovenia
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22
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Zabolotna Y, Volochnyuk DM, Ryabukhin SV, Gavrylenko K, Horvath D, Klimchuk O, Oksiuta O, Marcou G, Varnek A. SynthI: A New Open-Source Tool for Synthon-Based Library Design. J Chem Inf Model 2021; 62:2151-2163. [PMID: 34723532 DOI: 10.1021/acs.jcim.1c00754] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Most of the existing computational tools for de novo library design are focused on the generation, rational selection, and combination of promising structural motifs to form members of the new library. However, the absence of a direct link between the chemical space of the retrosynthetically generated fragments and the pool of available reagents makes such approaches appear as rather theoretical and reality-disconnected. In this context, here we present Synthons Interpreter (SynthI), a new open-source toolkit for de novo library design that allows merging those two chemical spaces into a single synthons space. Here synthons are defined as actual fragments with valid valences and special labels, specifying the position and the nature of reactive centers. They can be issued from either the "breakup" of reference compounds according to 38 retrosynthetic rules or real reagents, after leaving group withdrawal or transformation. Such an approach not only enables the design of synthetically accessible libraries and analog generation but also facilitates reagents (building blocks) analysis in the medicinal chemistry context. SynthI code is publicly available at https://github.com/Laboratoire-de-Chemoinformatique/SynthI.
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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 Kyiv, 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 Kyiv, Ukraine
| | - Kostiantyn Gavrylenko
- Research-And-Education ChemBioCenter, National Taras Shevchenko University of Kyiv, Chervonotkatska str., 61, 03022 Kyiv, Ukraine.,Enamine Ltd.78 Chervonotkatska str., 02660 Kyiv, Ukraine
| | - Dragos Horvath
- 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
| | - 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
| | - Gilles Marcou
- 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.,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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23
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Bennett S, Szczypiński FT, Turcani L, Briggs ME, Greenaway RL, Jelfs KE. Materials Precursor Score: Modeling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors. J Chem Inf Model 2021; 61:4342-4356. [PMID: 34388347 PMCID: PMC8479809 DOI: 10.1021/acs.jcim.1c00375] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Indexed: 11/30/2022]
Abstract
Computation is increasingly being used to try to accelerate the discovery of new materials. One specific example of this is porous molecular materials, specifically porous organic cages, where the porosity of the materials predominantly comes from the internal cavities of the molecules themselves. The computational discovery of novel structures with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realization. Attempts at experimental validation are often time-consuming, expensive, and frequently, the key bottleneck of material discovery. In this work, we developed a computational screening workflow for porous molecules that includes consideration of the synthetic difficulty of material precursors, aimed at easing the transition between computational prediction and experimental realization. We trained a machine learning model by first collecting data on 12,553 molecules categorized either as "easy-to-synthesize" or "difficult-to-synthesize" by expert chemists with years of experience in organic synthesis. We used an approach to address the class imbalance present in our data set, producing a binary classifier able to categorize easy-to-synthesize molecules with few false positives. We then used our model during computational screening for porous organic molecules to bias toward precursors whose easier synthesis requirements would make them promising candidates for experimental realization and material development. We found that even by limiting precursors to those that are easier-to-synthesize, we are still able to identify cages with favorable, and even some rare, properties.
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Affiliation(s)
- Steven Bennett
- Department
of Chemistry, Imperial College London, Molecular Sciences Research Hub,
White City Campus, Wood Lane, London W12 0BZ, U.K.
| | - Filip T. Szczypiński
- Department
of Chemistry, Imperial College London, Molecular Sciences Research Hub,
White City Campus, Wood Lane, London W12 0BZ, U.K.
| | - Lukas Turcani
- Department
of Chemistry, Imperial College London, Molecular Sciences Research Hub,
White City Campus, Wood Lane, London W12 0BZ, U.K.
| | - Michael E. Briggs
- Materials
Innovation Factory, University of Liverpool, 51 Oxford Street, Liverpool L7 3NY, U.K.
| | - Rebecca L. Greenaway
- Department
of Chemistry, Imperial College London, Molecular Sciences Research Hub,
White City Campus, Wood Lane, London W12 0BZ, U.K.
| | - Kim E. Jelfs
- Department
of Chemistry, Imperial College London, Molecular Sciences Research Hub,
White City Campus, Wood Lane, London W12 0BZ, U.K.
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24
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Szymanski NJ, Zeng Y, Huo H, Bartel CJ, Kim H, Ceder G. Toward autonomous design and synthesis of novel inorganic materials. MATERIALS HORIZONS 2021; 8:2169-2198. [PMID: 34846423 DOI: 10.1039/d1mh00495f] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve "closed-loop" materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Finally, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.
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Affiliation(s)
- Nathan J Szymanski
- Department of Materials Science & Engineering, UC Berkeley, Berkeley, CA 94720, USA.
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25
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Graff DE, Shakhnovich EI, Coley CW. Accelerating high-throughput virtual screening through molecular pool-based active learning. Chem Sci 2021; 12:7866-7881. [PMID: 34168840 PMCID: PMC8188596 DOI: 10.1039/d0sc06805e] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 04/26/2021] [Indexed: 12/13/2022] Open
Abstract
Structure-based virtual screening is an important tool in early stage drug discovery that scores the interactions between a target protein and candidate ligands. As virtual libraries continue to grow (in excess of 108 molecules), so too do the resources necessary to conduct exhaustive virtual screening campaigns on these libraries. However, Bayesian optimization techniques, previously employed in other scientific discovery problems, can aid in their exploration: a surrogate structure-property relationship model trained on the predicted affinities of a subset of the library can be applied to the remaining library members, allowing the least promising compounds to be excluded from evaluation. In this study, we explore the application of these techniques to computational docking datasets and assess the impact of surrogate model architecture, acquisition function, and acquisition batch size on optimization performance. We observe significant reductions in computational costs; for example, using a directed-message passing neural network we can identify 94.8% or 89.3% of the top-50 000 ligands in a 100M member library after testing only 2.4% of candidate ligands using an upper confidence bound or greedy acquisition strategy, respectively. Such model-guided searches mitigate the increasing computational costs of screening increasingly large virtual libraries and can accelerate high-throughput virtual screening campaigns with applications beyond docking.
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Affiliation(s)
- David E Graff
- Department of Chemistry and Chemical Biology, Harvard University Cambridge MA USA
| | - Eugene I Shakhnovich
- Department of Chemistry and Chemical Biology, Harvard University Cambridge MA USA
| | - Connor W Coley
- Department of Chemical Engineering, MIT Cambridge MA USA
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26
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Awale M, Hert J, Guasch L, Riniker S, Kramer C. The Playbooks of Medicinal Chemistry Design Moves. J Chem Inf Model 2021; 61:729-742. [PMID: 33522806 DOI: 10.1021/acs.jcim.0c01143] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Large databases of biologically relevant molecules, such as ChEMBL, SureChEMBL, or compound collections of pharmaceutical or agrochemical companies, are invaluable sources of medicinal chemistry information, albeit implicit. We developed a modified matched molecular pair approach to systematically and exhaustively extract the transformations in these databases and distill them into snippets of explicit design knowledge that are easily interpretable and directly applicable. The resulting "playbooks of medicinal chemistry design moves" capture the collective pharmaceutical and agrochemical research expertise across multiple chemists, companies, targets, and projects. They can be queried in an automated fashion for systematic prospective design and compound generation. The ChEMBL playbook and an application to exploit it are available at https://github.com/mahendra-awale/medchem_moves.
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Affiliation(s)
- Mahendra Awale
- Computer-Aided Drug Design/Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Jérôme Hert
- Computer-Aided Drug Design/Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Laura Guasch
- Computer-Aided Drug Design/Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Christian Kramer
- Computer-Aided Drug Design/Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
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27
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28
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Patel H, Ihlenfeldt WD, Judson PN, Moroz YS, Pevzner Y, Peach ML, Delannée V, Tarasova NI, Nicklaus MC. SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules. Sci Data 2020; 7:384. [PMID: 33177514 PMCID: PMC7658252 DOI: 10.1038/s41597-020-00727-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 10/16/2020] [Indexed: 01/08/2023] Open
Abstract
We have made available a database of over 1 billion compounds predicted to be easily synthesizable, called Synthetically Accessible Virtual Inventory (SAVI). They have been created by a set of transforms based on an adaptation and extension of the CHMTRN/PATRAN programming languages describing chemical synthesis expert knowledge, which originally stem from the LHASA project. The chemoinformatics toolkit CACTVS was used to apply a total of 53 transforms to about 150,000 readily available building blocks (enamine.net). Only single-step, two-reactant syntheses were calculated for this database even though the technology can execute multi-step reactions. The possibility to incorporate scoring systems in CHMTRN allowed us to subdivide the database of 1.75 billion compounds in sets according to their predicted synthesizability, with the most-synthesizable class comprising 1.09 billion synthetic products. Properties calculated for all SAVI products show that the database should be well-suited for drug discovery. It is being made publicly available for free download from https://doi.org/10.35115/37n9-5738.
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Affiliation(s)
- Hitesh Patel
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | | | - Philip N Judson
- Heather Lea, Bland Hill, Norwood, Harrogate, HG3 1TE, England
| | - Yurii S Moroz
- Enamine Ltd, 78 Chervonotkatska Street, Suite 1, Kyiv, 02094, Ukraine and Chemspace LLC, 85 Chervonotkatska Street, Suite 1, Kyiv, 02094, Ukraine
| | - Yuri Pevzner
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
- AbbVie, Inc., North Chicago, IL, 60064, USA
| | - Megan L Peach
- Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA
| | - Victorien Delannée
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | - Nadya I Tarasova
- Synthetic Biologics and Drug Discovery Group, Laboratory of Cancer Immunometabolism, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA
| | - Marc C Nicklaus
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, 21702, USA.
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29
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Grygorenko OO, Radchenko DS, Dziuba I, Chuprina A, Gubina KE, Moroz YS. Generating Multibillion Chemical Space of Readily Accessible Screening Compounds. iScience 2020; 23:101681. [PMID: 33145486 PMCID: PMC7593547 DOI: 10.1016/j.isci.2020.101681] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 09/17/2020] [Accepted: 10/10/2020] [Indexed: 11/25/2022] Open
Abstract
An approach to the generation of ultra-large chemical libraries of readily accessible (“REAL”) compounds is described. The strategy is based on the use of two- or three-step three-component reaction sequences and available starting materials with pre-validated chemical reactivity. After the preliminary parallel experiments, the methods with at least ∼80% synthesis success rate (such as acylation – deprotection – acylation of monoprotected diamines or amide formation – click reaction with functionalized azides) can be selected and used to generate the target chemical space. It is shown that by using only on the two aforementioned reaction sequences, a nearly 29-billion compound library is easily obtained. According to the predicted physico-chemical descriptor values, the generated chemical space contains large fractions of both drug-like and “beyond rule-of-five” members, whereas the strictest lead-likeness criteria (the so-called Churcher's rules) are met by the lesser part, which still exceeds 22 million. A strategy for ultra-large readily accessible (REAL) compound libraries is described Pre-validated two- or three-step three-component reaction sequences are used A 29-billion chemical space with ∼80% synthesis success rate has been easily obtained
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30
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Saldívar-González FI, Huerta-García CS, Medina-Franco JL. Chemoinformatics-based enumeration of chemical libraries: a tutorial. J Cheminform 2020; 12:64. [PMID: 33372622 PMCID: PMC7590480 DOI: 10.1186/s13321-020-00466-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 10/05/2020] [Indexed: 11/10/2022] Open
Abstract
Virtual compound libraries are increasingly being used in computer-assisted drug discovery applications and have led to numerous successful cases. This paper aims to examine the fundamental concepts of library design and describe how to enumerate virtual libraries using open source tools. To exemplify the enumeration of chemical libraries, we emphasize the use of pre-validated or reported reactions and accessible chemical reagents. This tutorial shows a step-by-step procedure for anyone interested in designing and building chemical libraries with or without chemoinformatics experience. The aim is to explore various methodologies proposed by synthetic organic chemists and explore affordable chemical space using open-access chemoinformatics tools. As part of the tutorial, we discuss three examples of design: a Diversity-Oriented-Synthesis library based on lactams, a bis-heterocyclic combinatorial library, and a set of target-oriented molecules: isoindolinone based compounds as potential acetylcholinesterase inhibitors. This manuscript also seeks to contribute to the critical task of teaching and learning chemoinformatics.
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Affiliation(s)
- Fernanda I. Saldívar-González
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510 Mexico, Mexico
| | - C. Sebastian Huerta-García
- School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510 Mexico, Mexico
| | - José L. Medina-Franco
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510 Mexico, Mexico
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31
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Bellmann L, Penner P, Rarey M. Topological Similarity Search in Large Combinatorial Fragment Spaces. J Chem Inf Model 2020; 61:238-251. [PMID: 33084338 DOI: 10.1021/acs.jcim.0c00850] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In similarity-driven virtual screening, molecular fingerprints are widely used to assess the similarity of all compounds contained in a chemical library to a query compound of interest. This similarity analysis is traditionally done for each member of the library individually. When encoding chemical spaces that surpass billions of compounds in size, it becomes impractical to enumerate all their products, let alone assess their similarity, deeming this approach impossible without investing a substantial amount of resources. In this work, we present a novel search algorithm named SpaceLight for topological fingerprint similarity searching in large, practically non-enumerable combinatorial fragment spaces. In contrast to existing methods, SpaceLight is able to utilize the combinatorial character of these chemical spaces for efficiency while maintaining a high correlation of the description of molecular similarity to well-known molecular fingerprints like ECFP. The resulting software is able to search prominent spaces like EnamineREAL with more than 10 billion compounds in seconds on a standard desktop computer.
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Affiliation(s)
- Louis Bellmann
- ZBH-Center for Bioinformatics, Research Group for Computational Molecular Design, Universität Hamburg, Bundesstraβe 43, Hamburg 20146, Germany
| | - Patrick Penner
- ZBH-Center for Bioinformatics, Research Group for Computational Molecular Design, Universität Hamburg, Bundesstraβe 43, Hamburg 20146, Germany
| | - Matthias Rarey
- ZBH-Center for Bioinformatics, Research Group for Computational Molecular Design, Universität Hamburg, Bundesstraβe 43, Hamburg 20146, Germany
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32
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Abstract
The identification of synthetic routes that end with the desired product is considered an inherently time-consuming process that is largely dependent on expert knowledge regarding a limited proportion of the entire reaction space. At present, emerging machine learning technologies are reformulating the process of retrosynthetic planning. This study aimed to discover synthetic routes backwardly from a given desired molecule to commercially available compounds. The problem is reduced to a combinatorial optimization task with the solution space subject to the combinatorial complexity of all possible pairs of purchasable reactants. We address this issue within the framework of Bayesian inference and computation. The workflow consists of the training of a deep neural network, which is used to forwardly predict a product of the given reactants with a high level of accuracy, followed by inversion of the forward model into the backward one via Bayes' law of conditional probability. Using the backward model, a diverse set of highly probable reaction sequences ending with a given synthetic target is exhaustively explored using a Monte Carlo search algorithm. With a forward model prediction accuracy of approximately 87%, the Bayesian retrosynthesis algorithm successfully rediscovered 81.8 and 33.3% of known synthetic routes of one-step and two-step reactions, respectively, with top-10 accuracy. Remarkably, the Monte Carlo algorithm, which was specifically designed for the presence of multiple diverse routes, often revealed a ranked list of hundreds of reaction routes to the same synthetic target. We also investigated the potential applicability of such diverse candidates based on expert knowledge of synthetic organic chemistry.
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Affiliation(s)
- Zhongliang Guo
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo 190-8562, Japan
| | - Stephen Wu
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo 190-8562, Japan.,The Graduate University for Advanced Studies, SOKENDAI, Tachikawa, Tokyo 190-8562, Japan
| | - Mitsuru Ohno
- Daicel Corporation, Kita-ku, Osaka 530-0011, Japan
| | - Ryo Yoshida
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo 190-8562, Japan.,The Graduate University for Advanced Studies, SOKENDAI, Tachikawa, Tokyo 190-8562, Japan.,National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan
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33
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Berenger F, Yamanishi Y. Ranking Molecules with Vanishing Kernels and a Single Parameter: Active Applicability Domain Included. J Chem Inf Model 2020; 60:4376-4387. [PMID: 32281797 DOI: 10.1021/acs.jcim.9b01075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In ligand-based virtual screening, high-throughput screening (HTS) data sets can be exploited to train classification models. Such models can be used to prioritize yet untested molecules, from the most likely active (against a protein target of interest) to the least likely active. In this study, a single-parameter ranking method with an Applicability Domain (AD) is proposed. In effect, Kernel Density Estimates (KDE) are revisited to improve their computational efficiency and incorporate an AD. Two modifications are proposed: (i) using vanishing kernels (i.e., kernel functions with a finite support) and (ii) using the Tanimoto distance between molecular fingerprints as a radial basis function. This construction is termed "Vanishing Ranking Kernels" (VRK). Using VRK on 21 HTS assays, it is shown that VRK can compete in performance with a graph convolutional deep neural network. VRK are conceptually simple and fast to train. During training, they require optimizing a single parameter. A trained VRK model usually defines an active AD. Exploiting this AD can significantly increase the screening frequency of a VRK model. Software: https://github.com/UnixJunkie/rankers. Data sets: https://zenodo.org/record/1320776 and https://zenodo.org/record/3540423.
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Affiliation(s)
- Francois Berenger
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Kawazu, 680-4 Iizuka, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Kawazu, 680-4 Iizuka, Japan
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34
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Abstract
DNA-encoded library (DEL) technology is a novel ligand identification strategy that allows the synthesis and screening of unprecedented chemical diversity more efficiently than conventional methods. However, no reports have been published to systematically study how to increase the diversity and improve the molecular property space that can be covered with DEL. This report describes the development and application of eDESIGNER, an algorithm that comprehensively generates all possible library designs, enumerates and profiles samples from each library and evaluates them to select the libraries to be synthesized. This tool utilizes suitable on-DNA chemistries and available building blocks to design and identify libraries with a pre-defined molecular weight distribution and maximal diversity compared with compound collections from other sources.
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35
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Coley CW, Eyke NS, Jensen KF. Autonomous Discovery in the Chemical Sciences Part I: Progress. Angew Chem Int Ed Engl 2020; 59:22858-22893. [DOI: 10.1002/anie.201909987] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Indexed: 01/05/2023]
Affiliation(s)
- Connor W. Coley
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Natalie S. Eyke
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
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36
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Coley CW, Eyke NS, Jensen KF. Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt. Angew Chem Int Ed Engl 2020. [DOI: 10.1002/ange.201909987] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Connor W. Coley
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Natalie S. Eyke
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
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37
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Roch LM, Häse F, Kreisbeck C, Tamayo-Mendoza T, Yunker LPE, Hein JE, Aspuru-Guzik A. ChemOS: An orchestration software to democratize autonomous discovery. PLoS One 2020; 15:e0229862. [PMID: 32298284 PMCID: PMC7161969 DOI: 10.1371/journal.pone.0229862] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 02/16/2020] [Indexed: 01/25/2023] Open
Abstract
The current Edisonian approach to discovery requires up to two decades of fundamental and applied research for materials technologies to reach the market. Such a slow and capital-intensive turnaround calls for disruptive strategies to expedite innovation. Self-driving laboratories have the potential to provide the means to revolutionize experimentation by empowering automation with artificial intelligence to enable autonomous discovery. However, the lack of adequate software solutions significantly impedes the development of self-driving laboratories. In this paper, we make progress towards addressing this challenge, and we propose and develop an implementation of ChemOS; a portable, modular and versatile software package which supplies the structured layers necessary for the deployment and operation of self-driving laboratories. ChemOS facilitates the integration of automated equipment, and it enables remote control of automated laboratories. ChemOS can operate at various degrees of autonomy; from fully unsupervised experimentation to actively including inputs and feedbacks from researchers into the experimentation loop. The flexibility of ChemOS provides a broad range of functionality as demonstrated on five applications, which were executed on different automated equipment, highlighting various aspects of the software package.
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Affiliation(s)
- Loïc M. Roch
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Florian Häse
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Christoph Kreisbeck
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Teresa Tamayo-Mendoza
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Lars P. E. Yunker
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Jason E. Hein
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alán Aspuru-Guzik
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
- Department of Chemistry and Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Canadian Institute of Advanced Research, Toronto, Ontario, Canada
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38
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Nicolaou CA, Watson IA, LeMasters M, Masquelin T, Wang J. Context Aware Data-Driven Retrosynthetic Analysis. J Chem Inf Model 2020; 60:2728-2738. [DOI: 10.1021/acs.jcim.9b01141] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Christos A. Nicolaou
- Discovery Chemistry, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Ian A. Watson
- Discovery Chemistry, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Mark LeMasters
- Research Chemistry IT, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Thierry Masquelin
- Discovery Chemistry, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Jibo Wang
- Discovery Chemistry, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
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39
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Godfrey AG, Michael SG, Sittampalam GS, Zahoránszky-Köhalmi G. A Perspective on Innovating the Chemistry Lab Bench. Front Robot AI 2020; 7:24. [PMID: 33501193 PMCID: PMC7805875 DOI: 10.3389/frobt.2020.00024] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 02/12/2020] [Indexed: 12/16/2022] Open
Abstract
Innovating on the design and function of the chemical bench remains a quintessential challenge of the ages. It requires a deep understanding of the important role chemistry plays in scientific discovery as well a first principles approach to addressing the gaps in how work gets done at the bench. This perspective examines how one might explore designing and creating a sustainable new standard for advancing automated chemistry bench itself. We propose how this might be done by leveraging recent advances in laboratory automation whereby integrating the latest synthetic, analytical and information technologies, and AI/ML algorithms within a standardized framework, maximizes the value of the data generated and the broader utility of such systems. Although the context of this perspective focuses on the design of advancing molecule of potential therapeutic value, it would not be a stretch to contemplate how such systems could be applied to other applied disciplines like advanced materials, foodstuffs, or agricultural product development.
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Affiliation(s)
- Alexander G. Godfrey
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, United States
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40
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Grygorenko OO, Volochnyuk DM, Ryabukhin SV, Judd DB. The Symbiotic Relationship Between Drug Discovery and Organic Chemistry. Chemistry 2019; 26:1196-1237. [PMID: 31429510 DOI: 10.1002/chem.201903232] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 08/19/2019] [Indexed: 12/20/2022]
Abstract
All pharmaceutical products contain organic molecules; the source may be a natural product or a fully synthetic molecule, or a combination of both. Thus, it follows that organic chemistry underpins both existing and upcoming pharmaceutical products. The reverse relationship has also affected organic synthesis, changing its landscape towards increasingly complex targets. This Review article sets out to give a concise appraisal of this symbiotic relationship between organic chemistry and drug discovery, along with a discussion of the design concepts and highlighting key milestones along the journey. In particular, criteria for a high-quality compound library design enabling efficient virtual navigation of chemical space, as well as rise and fall of concepts for its synthetic exploration (such as combinatorial chemistry; diversity-, biology-, lead-, or fragment-oriented syntheses; and DNA-encoded libraries) are critically surveyed.
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Affiliation(s)
- Oleksandr O Grygorenko
- Enamine Ltd., Chervonotkatska Street 78, Kiev, 02094, Ukraine.,Taras Shevchenko National University of Kiev, Volodymyrska Street 60, Kiev, 01601, Ukraine
| | - Dmitriy M Volochnyuk
- Enamine Ltd., Chervonotkatska Street 78, Kiev, 02094, Ukraine.,Taras Shevchenko National University of Kiev, Volodymyrska Street 60, Kiev, 01601, Ukraine.,Institute of Organic Chemistry, National Academy of Sciences of Ukraine, Murmanska Street 5, Kiev, 02660, Ukraine
| | - Sergey V Ryabukhin
- Enamine Ltd., Chervonotkatska Street 78, Kiev, 02094, Ukraine.,Taras Shevchenko National University of Kiev, Volodymyrska Street 60, Kiev, 01601, Ukraine
| | - Duncan B Judd
- Awridian Ltd., Stevenage Bioscience Catalyst, Gunnelswood Road, Stevenage, Herts, SG1 2FX, UK
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41
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Lessel U, Lemmen C. Comparison of Large Chemical Spaces. ACS Med Chem Lett 2019; 10:1504-1510. [PMID: 31620241 DOI: 10.1021/acsmedchemlett.9b00331] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 09/11/2019] [Indexed: 12/20/2022] Open
Abstract
Chemical libraries are commonplace in computer-aided drug discovery, and assessing their overlap/complementarity is a routine task. For this purpose, different techniques are applied, ranging from exact matching to comparing physicochemical properties. However, these techniques are applicable only if the compound sets are not too big. Particularly for chemical spaces, containing billions of compounds, alternative ways of assessment are required. Random subsets could be enumerated and compared one-to-one, but given the vast sizes of the chemical spaces assessed here, such samples can at best provide a rough estimate of any overlap. Here we describe a novel way to compare chemical spaces utilizing a panel of query compounds. We applied this technique to three different types of spaces and obtained insight into their structural overlap, their coverage of the chemical universe, and their density. As chemical feasibility of virtual compounds is particularly important, we included related in silico predictions in our assessment.
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Affiliation(s)
- Uta Lessel
- Department of Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach an der Riss, Germany
| | - Christian Lemmen
- BioSolveIT GmbH, An der Ziegelei 79, 53757 St. Augustin, Germany
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42
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Klingler FM, Gastreich M, Grygorenko OO, Savych O, Borysko P, Griniukova A, Gubina KE, Lemmen C, Moroz YS. SAR by Space: Enriching Hit Sets from the Chemical Space. Molecules 2019; 24:molecules24173096. [PMID: 31454992 PMCID: PMC6749418 DOI: 10.3390/molecules24173096] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 08/14/2019] [Accepted: 08/23/2019] [Indexed: 12/26/2022] Open
Abstract
We introduce SAR by Space, a concept to drastically accelerate structure-activity relationship (SAR) elucidation by synthesizing neighboring compounds that originate from vast chemical spaces. The space navigation is accomplished within minutes on affordable standard computer hardware using a tree-based molecule descriptor and dynamic programming. Maximizing the synthetic accessibility of the results from the computer is achieved by applying a careful selection of building blocks in combination with suitably chosen reactions; a decade of in-house quality control shows that this is a crucial part in the process. The REAL Space is the largest chemical space of commercially available compounds, counting 11 billion molecules as of today. It was used to mine actives against bromodomain 4 (BRD4). Before synthesis, compounds were docked into the binding site using a scoring function, which incorporates intrinsic desolvation terms, thus avoiding time-consuming simulations. Five micromolar hits have been identified and verified within less than six weeks, including the measurement of IC50 values. We conclude that this procedure is a substantial time-saver, accelerating both ligand and structure-based approaches in hit generation and lead optimization stages.
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Affiliation(s)
| | - Marcus Gastreich
- BioSolveIT GmbH, An der Ziegelei 79, 53757 Sankt Augustin, Germany
| | - Oleksandr O Grygorenko
- Enamine Ltd., Chervonotkatska Street 78, 02094 Kyiv, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, 01601 Kyiv, Ukraine
| | - Olena Savych
- Enamine Ltd., Chervonotkatska Street 78, 02094 Kyiv, Ukraine
| | - Petro Borysko
- Bienta/Enamine Ltd., Chervonotkatska Street 78, 02094 Kyiv, Ukraine
| | | | - Kateryna E Gubina
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, 01601 Kyiv, Ukraine
| | - Christian Lemmen
- BioSolveIT GmbH, An der Ziegelei 79, 53757 Sankt Augustin, Germany
| | - Yurii S Moroz
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, 01601 Kyiv, Ukraine.
- Chemspace, Ilukstes iela 38-5, LV-1082 Riga, Latvia.
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43
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Nicolaou CA, Humblet C, Hu H, Martin EM, Dorsey FC, Castle TM, Burton KI, Hu H, Hendle J, Hickey MJ, Duerksen J, Wang J, Erickson JA. Idea2Data: Toward a New Paradigm for Drug Discovery. ACS Med Chem Lett 2019; 10:278-286. [PMID: 30891127 DOI: 10.1021/acsmedchemlett.8b00488] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 02/04/2019] [Indexed: 12/14/2022] Open
Abstract
Increasing the success rate and throughput of drug discovery will require efficiency improvements throughout the process that is currently used in the pharmaceutical community, including the crucial step of identifying hit compounds to act as drivers for subsequent optimization. Hit identification can be carried out through large compound collection screening and often involves the generation and testing of many hypotheses based on available knowledge. In practice, hypothesis generation can involve the selection of promising chemical structures from compound collections using predictive models built from previous screening/assay results. Available physical collections, typically used during hit identification, are of the order of 106 compounds but represent only a small fraction of the small molecule drug-like chemical space. In an effort to survey a larger portion of chemical space and eliminate inefficiencies during hit identification, we introduce a new process, termed Idea2Data (I2D) that tightly integrates computational and experimental components of the drug discovery process. I2D provides the ability to connect a vast virtual collection of compounds readily synthesizable on automated synthesis systems with computational predictive models for the identification of promising structures. This new paradigm enables researchers to process billions of virtual molecules and select structures that can be prepared on automated systems and made available for biological testing, allowing for timely hypothesis testing and follow-up. Since its introduction, I2D has positively impacted several portfolio efforts through identification of new chemical scaffolds and functionalization of existing scaffolds. In this Innovations paper, we describe the I2D process and present an application for the discovery of new ULK inhibitors.
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Affiliation(s)
- Christos A. Nicolaou
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Christine Humblet
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Hong Hu
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Eva M. Martin
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Frank C. Dorsey
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Thomas M. Castle
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Keith Ian Burton
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Haitao Hu
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Jorg Hendle
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Michael J. Hickey
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Joel Duerksen
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Jibo Wang
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Jon A. Erickson
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
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The next level in chemical space navigation: going far beyond enumerable compound libraries. Drug Discov Today 2019; 24:1148-1156. [PMID: 30851414 DOI: 10.1016/j.drudis.2019.02.013] [Citation(s) in RCA: 127] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 02/01/2019] [Accepted: 02/28/2019] [Indexed: 10/27/2022]
Abstract
Recent innovations have brought pharmacophore-driven methods for navigating virtual chemical spaces, the size of which can reach into the billions of molecules, to the fingertips of every chemist. There has been a paradigm shift in the underlying computational chemistry that drives chemical space search applications, incorporating intelligent reaction knowledge into their core so that they can readily deliver commercially available molecules as nearest neighbor hits from within giant virtual spaces. These vast resources enable medicinal chemists to execute rapid scaffold-hopping experiments, rapid hit expansion, and structure-activity relationship (SAR) exploitation in largely intellectual property (IP)-free territory and at unparalleled low cost.
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45
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Baumann M. Integrating continuous flow synthesis with in-line analysis and data generation. Org Biomol Chem 2019; 16:5946-5954. [PMID: 30062354 DOI: 10.1039/c8ob01437j] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Continuous flow synthesis of fine chemicals has successfully advanced from an academic niche area to a rapidly growing field of its own that directly impacts developments and applications in industrial settings. Whilst the numerous advantages of flow over batch processing are widely recognised and have led to a wider uptake of continuous flow synthesis within the community, we have reached a point where continuous flow synthesis has to transition from a stand-alone enabling technology to a readily integrated synthesis concept. Thus it is paramount to embrace a multitude of in-line analysis and purification techniques to not only allow for efficiently telescoped multi-step sequences but ultimately generate bioactivity data concomitantly on newly synthesised entities. This short review summarises the state of the art in this field and presents both challenges and opportunities that arise from this ambitious endeavour.
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Affiliation(s)
- Marcus Baumann
- School of Chemistry, University College Dublin, Science Centre South, Belfield, Dublin 4, Ireland.
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46
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van Hilten N, Chevillard F, Kolb P. Virtual Compound Libraries in Computer-Assisted Drug Discovery. J Chem Inf Model 2019; 59:644-651. [PMID: 30624918 DOI: 10.1021/acs.jcim.8b00737] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The use of virtual compound libraries in computer-assisted drug discovery has gained in popularity and has already lead to numerous successes. Here, we examine key static and dynamic virtual library concepts that have been developed over the past decade. To facilitate the search for new drugs in the vastness of chemical space, there are still several hurdles to overcome, including the current difficulties in screening and parsing efficiency and the need for more reliable vendors and accurate synthesis prediction tools. These challenges should be tackled by both the developers of virtual libraries and by their users, in order for the exploration of chemical space to live up to its potential.
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Affiliation(s)
- Niek van Hilten
- Department of Pharmaceutical Chemistry , Philipps-University Marburg , Marbacher Weg 6 , 35032 Marburg , Germany
| | - Florent Chevillard
- Department of Pharmaceutical Chemistry , Philipps-University Marburg , Marbacher Weg 6 , 35032 Marburg , Germany
| | - Peter Kolb
- Department of Pharmaceutical Chemistry , Philipps-University Marburg , Marbacher Weg 6 , 35032 Marburg , Germany
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47
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Berenger F, Yamanishi Y. A Distance-Based Boolean Applicability Domain for Classification of High Throughput Screening Data. J Chem Inf Model 2019; 59:463-476. [PMID: 30567434 DOI: 10.1021/acs.jcim.8b00499] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
In Quantitative Structure-Activity Relationship (QSAR) modeling, one must come up with an activity model but also with an applicability domain for that model. Some existing methods to create an applicability domain are complex, hard to implement, and/or difficult to interpret. Also, they often require the user to select a threshold value, or they embed an empirical constant. In this work, we propose a trivial to interpret and fully automatic Distance-Based Boolean Applicability Domain (DBBAD) algorithm for category QSAR. In retrospective experiments on High Throughput Screening data sets, this applicability domain improves the classification performance and early retrieval of support vector machine and random forest based classifiers, while improving the scaffold diversity among top-ranked active molecules.
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Affiliation(s)
- Francois Berenger
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering , Kyushu Institute of Technology , 680-4 Kawazu , Iizuka , Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering , Kyushu Institute of Technology , 680-4 Kawazu , Iizuka , Japan.,PRESTO, Japan Science and Technology Agency , Kawaguchi , Saitama 332-0012 , Japan
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48
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Abstract
Advances in computer processing speed and storage capacity have enabled researchers to generate virtual chemical libraries containing billions of molecules. While these numbers appear large, they are only a small fraction of the number of organic molecules that could potentially be synthesized. This review provides an overview of recent advances in the generation and use of virtual chemical libraries in medicinal chemistry. We also consider the practical implications of these libraries in drug discovery programs and highlight a number of current and future challenges.
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Affiliation(s)
- W Patrick Walters
- Relay Therapeutics , 215 First Street , Cambridge , Massachusetts 02142 , United States
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49
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Panteleev J, Gao H, Jia L. Recent applications of machine learning in medicinal chemistry. Bioorg Med Chem Lett 2018; 28:2807-2815. [PMID: 30122222 DOI: 10.1016/j.bmcl.2018.06.046] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 06/24/2018] [Accepted: 06/26/2018] [Indexed: 12/20/2022]
Abstract
In recent decades, artificial intelligence and machine learning have played a significant role in increasing the efficiency of processes across a wide spectrum of industries. When it comes to the pharmaceutical and biotechnology sectors, numerous tools enabled by advancement of computer science have been developed and are now routinely utilized. However, there are many aspects of the drug discovery process, which can further benefit from refinement of computational methods and tools, as well as improvement of accessibility of these new technologies. In this review, examples of recent developments in machine learning application are described, which have the potential to impact different parts of the drug discovery and development flow scheme. Notably, new deep learning-based approaches across compound design and synthesis, prediction of binding, activity and ADMET properties, as well as applications of genetic algorithms are highlighted.
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Affiliation(s)
- Jane Panteleev
- Amgen Discovery Research, 360 Binney St., Cambridge, MA 02141, USA
| | - Hua Gao
- Amgen Discovery Research, 360 Binney St., Cambridge, MA 02141, USA
| | - Lei Jia
- Amgen Discovery Research, One Amgen Center Dr., Thousand Oaks, CA 91320, USA.
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50
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Valeur E, Jimonet P. New Modalities, Technologies, and Partnerships in Probe and Lead Generation: Enabling a Mode-of-Action Centric Paradigm. J Med Chem 2018; 61:9004-9029. [DOI: 10.1021/acs.jmedchem.8b00378] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Eric Valeur
- Medicinal Chemistry, Cardiovascular, Renal and Metabolism, IMED Biotech Unit, AstraZeneca, Pepparedsleden 1, Mölndal 431 83, Sweden
| | - Patrick Jimonet
- External Innovation Drug Discovery, Global Business Development & Licensing, Sanofi, 13 quai Jules Guesde, 94400 Vitry-sur-Seine, France
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