1
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Pikalyova K, Orlov A, Horvath D, Marcou G, Varnek A. Predicting S. aureus antimicrobial resistance with interpretable genomic space maps. Mol Inform 2024; 43:e202300263. [PMID: 38386182 DOI: 10.1002/minf.202300263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 01/15/2024] [Accepted: 02/08/2024] [Indexed: 02/23/2024]
Abstract
Increasing antimicrobial resistance (AMR) represents a global healthcare threat. To decrease the spread of AMR and associated mortality, methods for rapid selection of optimal antibiotic treatment are urgently needed. Machine learning (ML) models based on genomic data to predict resistant phenotypes can serve as a fast screening tool prior to phenotypic testing. Nonetheless, many existing ML methods lack interpretability. Therefore, we present a methodology for visualization of sequence space and AMR prediction based on the non-linear dimensionality reduction method - generative topographic mapping (GTM). This approach, applied to AMR data of >5000 S. aureus isolates retrieved from the PATRIC database, yielded GTM models with reasonable accuracy for all drugs (balanced accuracy values ≥0.75). The Generative Topographic Maps (GTMs) represent data in the form of illustrative maps of the genomic space and allow for antibiotic-wise comparison of resistant phenotypes. The maps were also found to be useful for the analysis of genetic determinants responsible for drug resistance. Overall, the GTM-based methodology is a useful tool for both the illustrative exploration of the genomic sequence space and AMR prediction.
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Affiliation(s)
- Karina Pikalyova
- Laboratoire de Chémoinformatique, UMR 7140, Université de Strasbourg, 1 rue Blaise Pascal, Strasbourg, 67000, France
| | - Alexey Orlov
- Laboratoire de Chémoinformatique, UMR 7140, Université de Strasbourg, 1 rue Blaise Pascal, Strasbourg, 67000, France
| | - Dragos Horvath
- Laboratoire de Chémoinformatique, UMR 7140, Université de Strasbourg, 1 rue Blaise Pascal, Strasbourg, 67000, France
| | - Gilles Marcou
- Laboratoire de Chémoinformatique, UMR 7140, Université de Strasbourg, 1 rue Blaise Pascal, Strasbourg, 67000, France
| | - Alexandre Varnek
- Laboratoire de Chémoinformatique, UMR 7140, Université de Strasbourg, 1 rue Blaise Pascal, Strasbourg, 67000, France
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2
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Pikalyova R, Zabolotna Y, Horvath D, Marcou G, Varnek A. Meta-GTM: Visualization and Analysis of the Chemical Library Space. J Chem Inf Model 2023; 63:5571-5582. [PMID: 37602843 DOI: 10.1021/acs.jcim.3c00719] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
In chemical library analysis, it may be useful to describe libraries as individual items rather than collections of compounds. This is particularly true for ultra-large noncherry-pickable compound mixtures, such as DNA-encoded libraries (DELs). In this sense, the chemical library space (CLS) is useful for the management of a portfolio of libraries, just like chemical space (CS) helps manage a portfolio of molecules. Several possible CLSs were previously defined using vectorial library representations obtained from generative topographic mapping (GTM). Given the steadily growing number of DEL designs, the CLS becomes "crowded" and requires analysis tools beyond pairwise library comparison. Therefore, herein, we investigate the cartography of CLS on meta-(μ)GTMs─"meta" to remind that these are maps of the CLS, itself based on responsibility vectors issued by regular CS GTMs. 2,5 K DELs and ChEMBL (reference) were projected on the μGTM, producing landscapes of library-specific properties. These describe both interlibrary similarity and intrinsic library characteristics in the same view, herewith facilitating the selection of the best project-specific libraries.
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Affiliation(s)
- Regina Pikalyova
- Laboratory of Chemoinformatics, University of Strasbourg, 4, rue B. Pascal, Strasbourg 67081, France
| | - Yuliana Zabolotna
- Laboratory of Chemoinformatics, University of Strasbourg, 4, rue B. Pascal, Strasbourg 67081, France
| | - Dragos Horvath
- Laboratory of Chemoinformatics, University of Strasbourg, 4, rue B. Pascal, Strasbourg 67081, France
| | - Gilles Marcou
- Laboratory of Chemoinformatics, University of Strasbourg, 4, rue B. Pascal, Strasbourg 67081, France
| | - Alexandre Varnek
- Laboratory of Chemoinformatics, University of Strasbourg, 4, rue B. Pascal, Strasbourg 67081, France
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3
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López-Pérez K, López-López E, Medina-Franco JL, Miranda-Quintana RA. Sampling and Mapping Chemical Space with Extended Similarity Indices. Molecules 2023; 28:6333. [PMID: 37687162 PMCID: PMC10489020 DOI: 10.3390/molecules28176333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/24/2023] [Accepted: 08/26/2023] [Indexed: 09/10/2023] Open
Abstract
Visualization of the chemical space is useful in many aspects of chemistry, including compound library design, diversity analysis, and exploring structure-property relationships, to name a few. Examples of notable research areas where the visualization of chemical space has strong applications are drug discovery and natural product research. However, the sheer volume of even comparatively small sub-sections of chemical space implies that we need to use approximations at the time of navigating through chemical space. ChemMaps is a visualization methodology that approximates the distribution of compounds in large datasets based on the selection of satellite compounds that yield a similar mapping of the whole dataset when principal component analysis on a similarity matrix is performed. Here, we show how the recently proposed extended similarity indices can help find regions that are relevant to sample satellites and reduce the amount of high-dimensional data needed to describe a library's chemical space.
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Affiliation(s)
- Kenneth López-Pérez
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL 32611, USA;
| | - Edgar López-López
- DIFACQUIM Research Group, Department of Pharmacy, National Autonomous University of Mexico, Mexico City 04510, Mexico;
- Department of Chemistry and Graduate Program in Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07000, Mexico
| | - José L. Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, National Autonomous University of Mexico, Mexico City 04510, Mexico;
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4
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Abstract
DNA-encoded libraries (DELs) are widely used in the discovery of drug candidates, and understanding their design principles is critical for accessing better libraries. Most DELs are combinatorial in nature and are synthesized by assembling sets of building blocks in specific topologies. In this study, different aspects of library topology were explored and their effect on DEL properties and chemical diversity was analyzed. We introduce a descriptor for DEL topological assignment (DELTA) and use it to examine the landscape of possible DEL topologies and their coverage in the literature. A generative topographic mapping analysis revealed that the impact of library topology on chemical space coverage is secondary to building block selection. Furthermore, it became apparent that the descriptor used to analyze chemical space dictates how structures cluster, with the effects of topology being apparent when using three-dimensional descriptors but not with common two-dimensional descriptors. This outcome points to potential challenges of attempts to predict DEL productivity based on chemical space analyses alone. While topology is rather inconsequential for defining the chemical space of encoded compounds, it greatly affects possible interactions with target proteins as illustrated in docking studies using NAD/NADP binding proteins as model receptors.
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Affiliation(s)
- William K Weigel
- Department of Medicinal Chemistry, Skaggs College of Pharmacy, University of Utah, 30 S 2000 E, Salt Lake City, Utah 84112, United States
| | - Alba L Montoya
- Department of Medicinal Chemistry, Skaggs College of Pharmacy, University of Utah, 30 S 2000 E, Salt Lake City, Utah 84112, United States
| | - Raphael M Franzini
- Department of Medicinal Chemistry, Skaggs College of Pharmacy, University of Utah, 30 S 2000 E, Salt Lake City, Utah 84112, United States
- Huntsman Cancer Institute, University of Utah, 2000 Circle of Hope Dr., Salt Lake City, Utah 84112, United States
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5
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Pikalyova R, Zabolotna Y, Horvath D, Marcou G, Varnek A. Chemical Library Space: Definition and DNA-Encoded Library Comparison Study Case. J Chem Inf Model 2023. [PMID: 37368824 DOI: 10.1021/acs.jcim.3c00520] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
The development of DNA-encoded library (DEL) technology introduced new challenges for the analysis of chemical libraries. It is often useful to consider a chemical library as a stand-alone chemoinformatic object─represented both as a collection of independent molecules, and yet an individual entity─in particular, when they are inseparable mixtures, like DELs. Herein, we introduce the concept of chemical library space (CLS), in which resident items are individual chemical libraries. We define and compare four vectorial library representations obtained using generative topographic mapping. These allow for an effective comparison of libraries, with the ability to tune and chemically interpret the similarity relationships. In particular, property-tuned CLS encodings enable to simultaneously compare libraries with respect to both property and chemotype distributions. We apply the various CLS encodings for the selection problem of DELs that optimally "match" a reference collection (here ChEMBL28), showing how the choice of the CLS descriptors may help to fine-tune the "matching" (overlap) criteria. Hence, the proposed CLS may represent a new efficient way for polyvalent analysis of thousands of chemical libraries. Selection of an easily accessible compound collection for drug discovery, as a substitute for a difficult to produce reference library, can be tuned for either primary or target-focused screening, also considering property distributions of compounds. Alternatively, selection of libraries covering novel regions of the chemical space with respect to a reference compound subspace may serve for library portfolio enrichment.
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Affiliation(s)
- Regina Pikalyova
- Laboratory of Chemoinformatics, University of Strasbourg, 4, rue B. Pascal, Strasbourg 67081, France
| | - Yuliana Zabolotna
- Laboratory of Chemoinformatics, University of Strasbourg, 4, rue B. Pascal, Strasbourg 67081, France
| | - Dragos Horvath
- Laboratory of Chemoinformatics, University of Strasbourg, 4, rue B. Pascal, Strasbourg 67081, France
| | - Gilles Marcou
- Laboratory of Chemoinformatics, University of Strasbourg, 4, rue B. Pascal, Strasbourg 67081, France
| | - Alexandre Varnek
- Laboratory of Chemoinformatics, University of Strasbourg, 4, rue B. Pascal, Strasbourg 67081, France
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6
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Medina‐Franco JL, Chávez‐Hernández AL, López‐López E, Saldívar‐González FI. Chemical Multiverse: An Expanded View of Chemical Space. Mol Inform 2022; 41:e2200116. [PMID: 35916110 PMCID: PMC9787733 DOI: 10.1002/minf.202200116] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 08/01/2022] [Indexed: 12/30/2022]
Abstract
Technological advances and practical applications of the chemical space concept in drug discovery, natural product research, and other research areas have attracted the scientific community's attention. The large- and ultra-large chemical spaces are associated with the significant increase in the number of compounds that can potentially be made and exist and the increasing number of experimental and calculated descriptors, that are emerging that encode the molecular structure and/or property aspects of the molecules. Due to the importance and continued evolution of compound libraries, herein, we discuss definitions proposed in the literature for chemical space and emphasize the convenience, discussed in the literature to use complementary descriptors to obtain a comprehensive view of the chemical space of compound data sets. In this regard, we introduce the term chemical multiverse to refer to the comprehensive analysis of compound data sets through several chemical spaces, each defined by a different set of chemical representations. The chemical multiverse is contrasted with a related idea: consensus chemical space.
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Affiliation(s)
- José L. Medina‐Franco
- DIFACQUIM research group, Department of Pharmacy, School of ChemistryNational Autonomous University of MexicoMexico City04510Mexico
| | - Ana L. Chávez‐Hernández
- DIFACQUIM research group, Department of Pharmacy, School of ChemistryNational Autonomous University of MexicoMexico City04510Mexico
| | - Edgar López‐López
- Department of PharmacologyCenter for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV)Mexico City07360Mexico
| | - Fernanda I. Saldívar‐González
- DIFACQUIM research group, Department of Pharmacy, School of ChemistryNational Autonomous University of MexicoMexico City04510Mexico
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7
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Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry. Int J Mol Sci 2022; 23:ijms23052797. [PMID: 35269939 PMCID: PMC8910896 DOI: 10.3390/ijms23052797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 02/27/2022] [Accepted: 02/27/2022] [Indexed: 12/10/2022] Open
Abstract
The availability of computers has brought novel prospects in drug design. Neural networks (NN) were an early tool that cheminformatics tested for converting data into drugs. However, the initial interest faded for almost two decades. The recent success of Deep Learning (DL) has inspired a renaissance of neural networks for their potential application in deep chemistry. DL targets direct data analysis without any human intervention. Although back-propagation NN is the main algorithm in the DL that is currently being used, unsupervised learning can be even more efficient. We review self-organizing maps (SOM) in mapping molecular representations from the 1990s to the current deep chemistry. We discovered the enormous efficiency of SOM not only for features that could be expected by humans, but also for those that are not trivial to human chemists. We reviewed the DL projects in the current literature, especially unsupervised architectures. DL appears to be efficient in pattern recognition (Deep Face) or chess (Deep Blue). However, an efficient deep chemistry is still a matter for the future. This is because the availability of measured property data in chemistry is still limited.
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8
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Pikalyova R, Zabolotna Y, Volochnyuk D, Horvath D, Gilles M, Varnek A. Exploration of the chemical space of DNA-encoded libraries. Mol Inform 2022; 41:e2100289. [PMID: 34981643 DOI: 10.1002/minf.202100289] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 01/03/2022] [Indexed: 11/09/2022]
Abstract
DNA-Encoded Library (DEL) technology has emerged as an alternative method for bioactive molecules discovery in medicinal chemistry. It enables the simple synthesis and screening of compound libraries of enormous size. Even though it gains more and more popularity each day, there are almost no reports of chemoinformatics analysis of DEL chemical space. Therefore, in this project, we aimed to generate and analyze the ultra-large chemical space of DEL. Around 2500 DELs were designed using commercially available BBs resulting in 2,5B DEL compounds that were compared to biologically relevant compounds from ChEMBL using Generative Topographic Mapping. This allowed to choose several optimal DELs covering the chemical space of ChEMBL to the highest extent and thus containing the maximum possible percentage of biologically relevant chemotypes. Different combinations of DELs were also analyzed to identify a set of mutually complementary libraries allowing to attain even higher coverage of ChEMBL than it is possible with one single DEL.
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9
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Goryashchenko AS, Uvarova VI, Osolodkin DI, Ishmukhametov AA. Discovery of small molecule antivirals targeting tick-borne encephalitis virus. ANNUAL REPORTS IN MEDICINAL CHEMISTRY 2022. [DOI: 10.1016/bs.armc.2022.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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10
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Zabolotna Y, Volochnyuk DM, Ryabukhin SV, Horvath D, Gavrilenko KS, Marcou G, Moroz YS, Oksiuta O, Varnek A. A Close-up Look at the Chemical Space of Commercially Available Building Blocks for Medicinal Chemistry. J Chem Inf Model 2021; 62:2171-2185. [PMID: 34928600 DOI: 10.1021/acs.jcim.1c00811] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The ability to efficiently synthesize desired compounds can be a limiting factor for chemical space exploration in drug discovery. This ability is conditioned not only by the existence of well-studied synthetic protocols but also by the availability of corresponding reagents, so-called building blocks (BBs). In this work, we present a detailed analysis of the chemical space of 400 000 purchasable BBs. The chemical space was defined by corresponding synthons─fragments contributed to the final molecules upon reaction. They allow an analysis of BB physicochemical properties and diversity, unbiased by the leaving and protective groups in actual reagents. The main classes of BBs were analyzed in terms of their availability, rule-of-two-defined quality, and diversity. Available BBs were eventually compared to a reference set of biologically relevant synthons derived from ChEMBL fragmentation, in order to illustrate how well they cover the actual medicinal chemistry needs. This was performed on a newly constructed universal generative topographic map of synthon chemical space that enables visualization of both libraries and analysis of their overlapped and library-specific regions.
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Affiliation(s)
- Yuliana Zabolotna
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Dmitriy M Volochnyuk
- Institute of Organic Chemistry, National Academy of Sciences of Ukraine, Murmanska Street 5, Kyiv 02660, Ukraine.,Enamine Ltd., 78 Chervonotkatska str., 02660 Kiev, Ukraine
| | - Sergey V Ryabukhin
- The Institute of High Technologies, Kyiv National Taras Shevchenko University, 64 Volodymyrska Street, Kyiv 01601, Ukraine.,Enamine Ltd., 78 Chervonotkatska str., 02660 Kiev, Ukraine
| | - Dragos Horvath
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Konstantin S Gavrilenko
- Research-And-Education ChemBioCenter, National Taras Shevchenko University of Kyiv, Chervonotkatska str., 61, 03022 Kiev, Ukraine.,Enamine Ltd., 78 Chervonotkatska str., 02660 Kiev, Ukraine
| | - Gilles Marcou
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France
| | - Yurii S Moroz
- Research-And-Education ChemBioCenter, National Taras Shevchenko University of Kyiv, Chervonotkatska str., 61, 03022 Kiev, Ukraine.,Chemspace, Chervonotkatska Street 78, 02094 Kyiv, Ukraine
| | - Oleksandr Oksiuta
- Institute of Organic Chemistry, National Academy of Sciences of Ukraine, Murmanska Street 5, Kyiv 02660, Ukraine.,Chemspace, Chervonotkatska Street 78, 02094 Kyiv, Ukraine
| | - Alexandre Varnek
- University of Strasbourg, Laboratoire de Chemoinformatique, 4, rue B. Pascal, Strasbourg 67081, France.,Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, 001-0021 Sapporo, Japan
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11
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Andronov M, Fedorov MV, Sosnin S. Exploring Chemical Reaction Space with Reaction Difference Fingerprints and Parametric t-SNE. ACS OMEGA 2021; 6:30743-30751. [PMID: 34805702 PMCID: PMC8600617 DOI: 10.1021/acsomega.1c04778] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/18/2021] [Indexed: 06/13/2023]
Abstract
Humans prefer visual representations for the analysis of large databases. In this work, we suggest a method for the visualization of the chemical reaction space. Our technique uses the t-SNE approach that is parameterized using a deep neural network (parametric t-SNE). We demonstrated that the parametric t-SNE combined with reaction difference fingerprints could provide a tool for the projection of chemical reactions on a low-dimensional manifold for easy exploration of reaction space. We showed that the global reaction landscape projected on a 2D plane corresponds well with the already known reaction types. The application of a pretrained parametric t-SNE model to new reactions allows chemists to study these reactions in a global reaction space. We validated the feasibility of this approach for two commercial drugs, darunavir and montelukast. We believe that our method can help to explore reaction space and will inspire chemists to find new reactions and synthetic ways.
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Affiliation(s)
- Mikhail Andronov
- Faculty
of Fundamental Physical and Chemical Engineering, Lomonosov Moscow State University, Leninskie gory, 1, Moscow 119991, Russian Federation
| | - Maxim V. Fedorov
- Sirius
University of Science and Technology, Olimpiysky Ave. b.1, Sochi 354000, Russian Federation
- Syntelly
LLC, Bolshoy Boulevard
30, bld. 1, Moscow 121205, Russian Federation
- Skolkovo
Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, Moscow 121205, Russian
Federation
| | - Sergey Sosnin
- Syntelly
LLC, Bolshoy Boulevard
30, bld. 1, Moscow 121205, Russian Federation
- Skolkovo
Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, Moscow 121205, Russian
Federation
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12
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Serafim MSM, Dos Santos Júnior VS, Gertrudes JC, Maltarollo VG, Honorio KM. Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade. Expert Opin Drug Discov 2021; 16:961-975. [PMID: 33957833 DOI: 10.1080/17460441.2021.1918098] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Introduction: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals.Areas covered: The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area.Expert opinion: From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.
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Affiliation(s)
- Mateus Sá Magalhães Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | | | - Jadson Castro Gertrudes
- Departamento de Computação, Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto (UFOP), Ouro Preto, Brazil
| | - Vinícius Gonçalves Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Kathia Maria Honorio
- Escola de Artes, Ciências e Humanidades, Universidade de São Paulo (USP), São Paulo, Brazil.,Centro de Ciências Naturais e Humanas, Universidade Federal do ABC (UFABC), Santo André, Brazil
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13
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Singh N, Villoutreix BO. Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises. Comput Struct Biotechnol J 2021; 19:2537-2548. [PMID: 33936562 PMCID: PMC8074526 DOI: 10.1016/j.csbj.2021.04.059] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/22/2021] [Accepted: 04/24/2021] [Indexed: 12/11/2022] Open
Abstract
There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.
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Affiliation(s)
- Natesh Singh
- Université de Paris, Inserm UMR 1141 NeuroDiderot, Robert-Debré Hospital, 75019 Paris, France
| | - Bruno O. Villoutreix
- Université de Paris, Inserm UMR 1141 NeuroDiderot, Robert-Debré Hospital, 75019 Paris, France
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