Introduction
Drug development is a complex, risky, expensive and time-consuming process that requires the accurate execution of multiple stages, from identification and selection of collections of potentially druggable molecules to proof-of-concept validation. Prior to embarking on a drug discovery project, a detailed strategic plan must be designed that includes hundreds of critical considerations such as source of compounds for screening, feasibility of their synthetic pathways, target selection (molecules, cells, organisms), type of output (binding, function, phenotype), throughput, nature, layers and iterations of the screening process, scoring systems, lead optimization approaches or model systems for validation (biochemical activity, cellular function, organismal properties) and, crucially, good contingency plans.
The need to discover new drugs rests on practical matters of human progress, rather than mere market considerations. For example, the global emergence of multidrug resistant pathogens makes it a research target to find alternative antibiotics; current cancer drugs, including advanced biologicals, face drug resistance; many parasitic diseases lack effective drug treatments; highly prevalent neurodegenerative diseases and rare diseases, which collectively affect significant numbers of people, are essentially drug orphan; vital agricultural crops are plagued by fungal and parasitic diseases that have evolved to become increasingly resistant to currently available chemicals. The current COVID-19 pandemic illustrates how pharmaceutically unprepared humanity is (vaccines aside) to confront the sudden emergence of a novel, deadly and highly transmissible pathogen. To date, only a handful of repurposed drugs display demonstrable therapeutic efficacy against infection and disease by the causing agent, SARS-CoV-2 (; Sandulescu et al., 2023). Despite an unprecedented parallel effort by hundreds of thousands of industrial and academic scientists worldwide, employing leading-edge technologies, no new drugs have been discovered over the past 3 years to effectively treat this disease.
Natural products as sources of chemical diversity
That some of these targets and diseases may be truly undruggable remains a possibility. However, the general working hypothesis is that small molecules or biologicals will be eventually found to match the majority of designated new targets and to significantly improve upon existing drugs that act on more conventional targets. Although this tenet may seem like wishful thinking, it is at least partly based on sound estimates of ligand structural diversity and druggable chemical space, as well as on the evidence that combinatorial approaches remain largely untested. As such, while the largest currently available compound and fragment libraries, used in ultra-large virtual screenings, contain up to 2 × 109 unique structures (; ; ), the total chemical space of small organic compounds suitable for drug discovery is estimated as more than 1060 molecules (). The underlying concept is that the more compounds are screened, the higher the likelihood of finding true positives (; ).
The above considerations suggest that there is still ample margin to finding new structures to be used as ligands for drug discovery screening efforts, which begs the question: Where will new chemical entities (NCEs) likely come from? Because of their special features as compared to currently available synthetic molecules, natural products (NPs) offer advantages as sources of future NCEs. As such, NPs provide large scaffold diversity and structural complexity accompanied with generally higher molecular rigidity, more chiral centers, higher fraction of sp3 atoms, more oxygen atoms and hydrogen bond acceptors and donors, lower octanol–water partition coefficients (cLog) indicating higher hydrophilicity, low ratio of aromatic ring atoms or diversity of ring systems (; ; Najmi et al., 2022).
A major limitation for expanding the ligand chemical space with NPs is the laborious nature of NP isolation and structural characterization towards drug discovery. Traditionally, this is done through producing crude extracts with a variety of solvents, screened and fractionated guided by biological activity, and hit compounds purified and structurally characterized. Given the availability of large compound structural databases, a virtual screening-centric strategy may afford to reverse conventional drug discovery schemes. As such, compounds can be structurally characterized after minimal purification or fractionation from crude extracts, by means of NMR spectroscopy, high-resolution mass spectrometry (HRMS), liquid chromatography HRMS (LC–HRMS) (; Wolfender et al., 2019; ; Stavrianidi, 2020). These methods enable routine acquisition of accurate molecular mass information and unambiguous assignment of formulae for hundreds to thousands of metabolites in a single extract over a broad dynamic range (), thus facilitating chemical entity dereplication (). In turn, dereplication is aided by accessing databases such as the Dictionary of Natural Products (https://dnp.chemnetbase.com/), which encompasses all NP structures reported with links to their biological sources, the Global Natural Products Social (GNPS) molecular networking platform (https://gnps.ucsd.edu/) (Wang M. et al., 2016), in which thousands of sets of MS/MS data are recorded from a given set of extracts, clustering compounds by their structural relationships (; Zhou et al., 2017; ), Compound Structure Identification (CSI) () or METLIN (), containing fragment ion spectra that can be used for the identification of unknown compounds. In summary, quantitative NMR and LC–MS approaches can yield novel structures to populate screening-ready databases at early stages in virtual screening drug discovery strategies, thus avoiding futile downstream development efforts (Wohlgemuth et al., 2016).
In spite of these technological advances that facilitate the expansion of the known chemical space, NPs may contain only a fraction of the theoretical space and scaffold diversity (Pye et al., 2017). In order to further expand chemical space and structural diversity, several strategies have been used to create new biologically active compounds by adding appendages on NP core scaffolds (), such as diversity-oriented synthesis (DOS) (Schreiber, 2009), DNA encoded libraries (DEL) () or biology-oriented synthesis (BOS) (van Hattum and Waldmann, 2014). Other strategies go beyond the available NP scaffolds by resorting to ring distortion reactions (Motika and Hergenrother, 2020), albeit still relying on the original scaffolds. The pseudo-NP strategy deconstructs NPs into fragments and recombines them into novel scaffolds that are not possible to attain through known biosynthetic pathways but retain the chemical and biological relevance of NPs (; ).
Additional efforts to expand the NP chemical space include engineering biosynthetic pathways aimed at yielding new NP analogues with potentially improved pharmacological properties (). Such strategies include the activation of cryptic or occult biosynthetic gene clusters that remain otherwise silent (), which can be achieved through the manipulation of culture conditions (Pan et al., 2019), micro-organism co-cultures () or exposure to small molecule epigenetic modulators (Pillay et al., 2022), among other approaches. The expansion of chemical space through various strategies entails the parallel development of new chemical methods capable of solving previously untested synthetic paths, so as to produce compounds corresponding to the newly designed structures and in cost-effective yields ().
Further to approaching theoretical ligand chemical space limits and structural characterization of NP and NP-like molecules, a major challenge is to make them available as large screening-ready libraries. Several databases provide information on NPs and their structures (Table 1). Compared to these databases, the currently accessible databases of chemical entities with focus on NPs of Latin American origin contain information on relatively few compounds (reviewed in (Medina-Franco, 2020; Nunez et al., 2021; )) (Table 2). As such, given the estimated share of Latin American biodiversity in global biodiversity (Raven et al., 2020), it is apparent that NPs of Latin American origin are heavily underrepresented in databases of NP physicochemical properties and structures.
TABLE 1
| Database | URL/References | Number of NPs |
|---|---|---|
| PubChem | https://pubchem.ncbi.nlm.nih.gov/(Wang et al., 2009) | 1 × 108 (synthetic and NP) |
| SuperNatural | https://bioinf-applied.charite.de/supernatural_3/; ; | 4.5 × 105 |
| COCONUT | https://coconut.naturalproducts.net/Sorokina et al. (2021) | 4.0 × 105 |
| Dictionary of Natural Products | https://dnp.chemnetbase.com/ | 3 × 105 |
| ChEMBL | https://www.ebi.ac.uk/chembl/(Gaulton et al., 2017) | 2.4 × 106 |
| Natural Products Atlas | https://www.npatlas.org/Wang et al. (2016a) | 2.4 × 105 |
| NAPRALERT | https://napralert.org/ | 3.0 × 105 |
| MarinLit | http://pubs.rsc.org/marinlit/) | 2.7 × 104 |
| TCM Database@Taiwan | https://tcm.cmu.edu.tw/ | 6.4 × 104 |
Natural Product databases containing NP structural information.
TABLE 2
| Database | URL/References | Number of NPs |
|---|---|---|
| LNMol | http://lnmol.iq.usp.br/ | >5,000 |
| ChEMBL-NTD | https://www.ebi.ac.uk/chemblntd/ | >10,000 |
| NuBBE DB | http://nubbe.iq.unesp.br/ | >1,500 |
| BRENDA NPAtlas | https://www.brenda-enzymes.org/npatlas/index.php | >3,000 |
| UEFS | http://zinc12.docking.org/catalogs/uefsnp | 503 |
| BIOFACQUIM | Sánchez-Cruz et al. (2020) | 553 |
| CIFLORPAN | Olmedo et al. (2017) | 450 |
Databases of chemical entities with focus on NPs of Latin American origin.
Large chemical structure databases are necessary for next-generation virtual drug discovery efforts, but they are not sufficient. Open-source, robust platforms are also needed that can integrate tasks in virtual screening and provide smooth connectivity to docking tools, such as VirtualFlow (), which can dock 1 billion compounds in about 2 weeks when run on 10,000 CPU cores, or V-SYNTHES (Sadybekov et al., 2022), which performs iterative steps of library preparation, enumeration, docking and hit selection, handling fragment-like libraries representing all possible scaffold–synthon combinations for all reactions in the 11 billion compound REAL Space library.
Novel approaches to unbiased high-throughput target identification
Experimental approaches for target identification require molecular and biochemical studies of disease pathophysiology (McFedries et al., 2013; Shaker et al., 2021; Zecha et al., 2023), which can be costly, labor-intensive and time-consuming. Conventional virtual screening approaches for target selection focus on a preferred molecular target to conduct structure-directed screenings. For better outcomes, such targets need to be structurally resolved at the highest possible atomic resolution. Until recently, that entailed “one target at a time” strategies. For decades, conventional approaches to the resolution of macromolecular structures have relied on techniques such X-ray crystallography or NMR, which are labor-intensive and low-throughput. The advent to fruition of cryoelectron microscopy () has enormously speeded up this process. As a result of these collective efforts, there are currently over 200,000 experimentally resolved structures deposited in Protein Data Bank (https://www.rcsb.org/), as unique entries corresponding to full-length proteins, fragments and complexes (protein-protein, protein-DNA, protein-ligand). This volume of structural information has laid the foundation for, and enabled, the use of machine learning tools, such as AlphaFold2 () or RoseTTA fold (), to accurately predict the structures of millions of proteins. As such, the AlphaFold protein structure database (https://alphafold.ebi.ac.uk) currently contains 214,683,829 predicted structures, including 48 complete proteomes. An added bonus to these predictive tools is that targets for which the experimentally determined structures are incomplete or ambiguous at specific regions can be completed or “polished” for subsequent use in virtual screening. For virtual drug discovery, potential binding sites must be defined on target proteins. To this end, a number of tools have been developed to predict pockets amenable to blocking by small drug-like molecules on proteins with known () or predicted (Wang et al., 2022; Sim et al., 2023) structures.
While the availability of large ligand structural libraries improves hit rates on pre-determined targets, the availability of large target structural libraries covering complete proteomes allows to perform near-complete screenings of hit and lead compounds for target selectivity. A major reason for candidate compound failure in drug discovery schemes is undesired or adverse effects, which is why characterization of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of candidate molecules at the earliest possible stage is relevant (Selick et al., 2002; ; Wu et al., 2020). Traditional ADMET prediction methods, such as quantitative structure activity relationship (QSAR) models, require costly and time-consuming data generation and are generally used relatively late in drug discovery programs. The increasing availability of data and resources enables ADMET predictions earlier in the process, with the use of machine learning tools to predict drug-target interactions, the blood-brain-barrier permeability of compounds, or toxic properties of drug candidates (reviewed in (Shaker et al., 2021)). The availability of predicted structures for complete proteomes should represent a paradigm shift in ADMET predictions, by affording approaches such as large-scale reverse docking, by which small molecules are simultaneously docked on many protein and cavity targets. This provides information on selectivity and thus potential off-target effects of the small molecules. For example, Wong et al. (2022) docked 319 compounds, of which 218 had antibacterial activity, on 296 essential E. coli proteins with structures predicted with AlphaFold2, finding unexpectedly promiscuous interactions and demonstrating the feasibility of the approach, albeit also highlighting the need to improve the performance of machine learning-based protein-ligand modeling methods.
Target-agnostic approaches have been applied as exploratory efforts to identify activities of interest prior to targeted drug discovery (Wang Y. et al., 2016). As such, metabolomics data can be integrated with data obtained by other omics techniques such as transcriptomics, proteomics or functional genomics with imaging-based or phenotypic screens (; ; ; Subramanian et al., 2017; ; Setten et al., 2019; Ziegler et al., 2021). Eventually, as current criteria followed by drug approval agencies require the identification of molecular mechanisms, these exploratory approaches need to be followed up by biochemical, molecular and structural studies for a precise mechanistic characterizations of candidate drug activities.
Perspectives and proposal
Significant constraints for the implementation of effective drug discovery programs in Latin America include relatively limited funding and failure to assemble coordinated transnational efforts. To date, scarce numbers of virtual screening projects in Latin America have led to the discovery of NP or NP-inspired compounds from isolation to proof-of-concept experimental activities of identified compounds (; Rodrigues et al., 2019; ; ; ; ; Vargas et al., 2021; Valera-Vera et al., 2022; ; ; ; ; Peralta-Moreno et al., 2023). With the increasing availability and accessibility of advanced virtual screening tools that facilitate many stages in drug discovery pipelines (; ; ; Singh et al., 2021; ; ; ; Muller et al., 2022; Sarkar et al., 2023; Thomas et al., 2023), which under conventional schemes are costly, labor intensive and time-consuming, a window of opportunity opens to change the tide towards NP-inspired drug discovery in less affluent economies.
Although less costly than conventional approaches, large next-generation virtual screening-centric drug discovery efforts based on NPs still require expertise and equipment for modern compound isolation and structural characterization, chemical synthetic and biosynthetic capabilities and, most importantly, ample computing power and connectivity. A further practical issue is the availability and cost of NPs and NP-inspired compounds for experimental validation of candidate molecules identified by virtual screening. The cost of NPs through conventional commercial channels can be relatively high, particularly for those with low yields in standard isolation procedures. As argued above, current technology enables early structural characterization of individual compounds, even as part of relatively complex mixtures, thus affording to bypass purification prior to structural characterization. In this scheme, NPs provide structures of interest, while experimental activity validation is performed with synthetic compounds that recapitulate NP structural features of pharmacological interest. This approach reduces the problem of yield, but does not totally solve the issue of cost per compound to be tested, particularly for those that may require difficult synthetic paths. New, more cost-effective synthetic strategies are expected to mitigate cost issues, as will entrusting non-profit institutions with on-demand synthesis of NP-inspired compounds for drug discovery. Together with building strong computational capabilities, this requires a concerted effort by individual teams, academic and industry organizations, transnational societies, institutional instances and public and private funding agencies, to design long-term, outcome-oriented plans coupled to commensurate multi-year funding schemes.
Statements
Author contributions
The author confirms being the sole contributor of this work and has approved it for publication.
Funding
The author’s work is supported by the Spanish Ministry of Science and Technology (PID 2019-107139RB-C21), the Interdisciplinary Platform-Global Health (Plataforma Temática Interdisciplinar-Salud Global, PTI-SG) (SGL2103019) and the Networked Researched Center on Liver and Digestive Diseases (Centro de Investigación en Red en Enfermedades Hepáticas y Digestivas, CIBER-EHD).
Conflict of interest
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1
AdessiT. G.CanteroJ.Ballesteros-CasallasA.GarciaM. E.NicotraV. E.PaulinoM. (2023). Identification of potential biological target for trypanocidal sesquiterpene lactones derivatives. J. Biomol. Struct. Dyn.2023, 1–14. 10.1080/07391102.2023.2183031
2
AksenovA. A.Da SilvaR.KnightR.LopesN. P.DorresteinP. C. (2017). Global chemical analysis of biology by mass spectrometry. Nat. Rev. Chem.1, 0054. 10.1038/s41570-017-0054
3
AllardP. M.PeresseT.BissonJ.GindroK.MarcourtL.PhamV. C.et al (2016). Integration of molecular networking and in-silico MS/MS fragmentation for natural products dereplication. Anal. Chem.88, 3317–3323. 10.1021/acs.analchem.5b04804
4
AlmeidaE. S. F. H.SilvaA. R. N.De OliveiraT. J. S.GuimaraesA. L.De AzevedoF. R.Brito Dos SantosM.et al (2023). A chalcone identified by in silico and in vitro assays possesses high larvicidal activity against Aedes aegypti. Acta Trop.238, 106791. 10.1016/j.actatropica.2022.106791
5
AraujoS. C.De AngeloR. M.BarbosaH.Costa-SilvaT. A.TemponeA. G.LagoJ. H. G.et al (2023). Identification of inhibitors as drug candidates against Chagas disease. Eur. J. Med. Chem.248, 115074. 10.1016/j.ejmech.2022.115074
6
AroraN.BanerjeeA. K. (2019). Dereplication in natural product discovery. Curr. Top. Med. Chem.19, 101–102. 10.2174/156802661902190328145951
7
Arul MuruganN.Ruba PriyaG.Narahari SastryG.MarkidisS. (2022). Artificial intelligence in virtual screening: Models versus experiments. Drug Discov. Today27, 1913–1923. 10.1016/j.drudis.2022.05.013
8
AtanasovA. G.ZotchevS. B.DirschV. M.International Natural Product SciencesT.SupuranC. T. (2021). Natural products in drug discovery: Advances and opportunities. Nat. Rev. Drug Discov.20, 200–216. 10.1038/s41573-020-00114-z
9
BaekM.DimaioF.AnishchenkoI.DauparasJ.OvchinnikovS.LeeG. R.et al (2021). Accurate prediction of protein structures and interactions using a three-track neural network. Science373, 871–876. 10.1126/science.abj8754
10
BanerjeeP.ErehmanJ.GohlkeB. O.WilhelmT.PreissnerR.DunkelM. (2015). Super Natural II--a database of natural products. Nucleic Acids Res.43, D935–D939. 10.1093/nar/gku886
11
BattiniL.FidalgoD. M.AlvarezD. E.BolliniM. (2021). Discovery of a potent and selective chikungunya virus envelope protein inhibitor through computer-aided drug design. ACS Infect. Dis.7, 1503–1518. 10.1021/acsinfecdis.0c00915
12
BaumeisterW. (2022). Cryo-electron tomography: A long journey to the inner space of cells. Cell185, 2649–2652. 10.1016/j.cell.2022.06.034
13
BelgamoJ. A.AlbercaL. N.PorfidoJ. L.RomeroF. N. C.RodriguezS.TaleviA.et al (2020). Application of target repositioning and in silico screening to exploit fatty acid binding proteins (FABPs) from Echinococcus multilocularis as possible drug targets. J. Comput. Aided Mol. Des.34, 1275–1288. 10.1007/s10822-020-00352-8
14
BertrandS.BohniN.SchneeS.SchumppO.GindroK.WolfenderJ. L. (2014). Metabolite induction via microorganism co-culture: A potential way to enhance chemical diversity for drug discovery. Biotechnol. Adv.32, 1180–1204. 10.1016/j.biotechadv.2014.03.001
15
BhagavatR.SankarS.SrinivasanN.ChandraN. (2018). An augmented pocketome: Detection and analysis of small-molecule binding pockets in proteins of known 3D structure. Structure26, 499–512. 10.1016/j.str.2018.02.001
16
Blanes-MiraC.Fernandez-AguadoP.De Andres-LopezJ.Fernandez-CarvajalA.Ferrer-MontielA.Fernandez-BallesterG. (2022). Comprehensive survey of consensus docking for high-throughput virtual screening. Molecules28, 175. 10.3390/molecules28010175
17
BluntJ. W.CarrollA. R.CoppB. R.DavisR. A.KeyzersR. A.PrinsepM. R. (2018). Marine natural products. Nat. Prod. Rep.35, 8–53. 10.1039/c7np00052a
18
BohacekR. S.McmartinC.GuidaW. C. (1996). The art and practice of structure-based drug design: A molecular modeling perspective. Med. Res. Rev.16, 3–50. 10.1002/(SICI)1098-1128(199601)16:1<3::AID-MED1>3.0.CO;2-6
19
BrayM. A.SinghS.HanH.DavisC. T.BorgesonB.HartlandC.et al (2016). Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat. Protoc.11, 1757–1774. 10.1038/nprot.2016.105
20
CaiJ. H.ZhuX. Z.GuoP. Y.RoseP.LiuX. T.LiuX.et al (2023). Recent updates in click and computational chemistry for drug discovery and development. Front. Chem.11, 1114970. 10.3389/fchem.2023.1114970
21
CaldwellG. W.YanZ.TangW.DasguptaM.HastingB. (2009). ADME optimization and toxicity assessment in early- and late-phase drug discovery. Curr. Top. Med. Chem.9, 965–980. 10.2174/156802609789630929
22
ChenC. Y. (2011). TCM Database@Taiwan: The world's largest traditional Chinese medicine database for drug screening in silico. PLoS One6, e15939. 10.1371/journal.pone.0015939
23
CrunkhornS. (2022). Screening ultra-large virtual libraries. Nat. Rev. Drug Discov.21, 95. 10.1038/d41573-022-00002-8
24
Da SilvaR. R.WangM.NothiasL. F.Van Der HooftJ. J. J.Caraballo-RodriguezA. M.FoxE.et al (2018). Propagating annotations of molecular networks using in silico fragmentation. PLoS Comput. Biol.14, e1006089. 10.1371/journal.pcbi.1006089
25
DainaA.ZoeteV. (2019). Application of the SwissDrugDesign online resources in virtual screening. Int. J. Mol. Sci.20, 4612. 10.3390/ijms20184612
26
DunkelM.FullbeckM.NeumannS.PreissnerR. (2006). SuperNatural: A searchable database of available natural compounds. Nucleic Acids Res.34, D678–D683. 10.1093/nar/gkj132
27
EarlD. C.FerrellP. B.Jr.LeelatianN.FroeseJ. T.ReismanB. J.IrishJ. M.et al (2018). Discovery of human cell selective effector molecules using single cell multiplexed activity metabolomics. Nat. Commun.9, 39. 10.1038/s41467-017-02470-8
28
FernandesD. A.BarrosR. P. C.TelesY. C. F.OliveiraL. H. G.LimaJ. B.ScottiM. T.et al (2019). Larvicidal compounds extracted from helicteres velutina K. Schum (sterculiaceae) evaluated against Aedes aegypti L. Molecules24, 2315. 10.3390/molecules24122315
29
FernandezG. A.CastroE. F.RosasR. A.FidalgoD. M.AdlerN. S.BattiniL.et al (2020). Design and optimization of quinazoline derivatives: New non-nucleoside inhibitors of bovine viral diarrhea virus. Front. Chem.8, 590235. 10.3389/fchem.2020.590235
30
FerreiraL. T.BorbaJ. V. B.Moreira-FilhoJ. T.RimoldiA.AndradeC. H.CostaF. T. M. (2021). QSAR-based virtual screening of natural products database for identification of potent antimalarial hits. Biomolecules11, 459. 10.3390/biom11030459
31
FontanaA.IturrinoL.CorensD.CregoA. L. (2020). Automated open-access liquid chromatography high resolution mass spectrometry to support drug discovery projects. J. Pharm. Biomed. Anal.178, 112908. 10.1016/j.jpba.2019.112908
32
FranziniR. M.RandolphC. (2016). Chemical space of DNA-encoded libraries. J. Med. Chem.59, 6629–6644. 10.1021/acs.jmedchem.5b01874
33
GalloK.KemmlerE.GoedeA.BeckerF.DunkelM.PreissnerR.et al (2023). SuperNatural 3.0-a database of natural products and natural product-based derivatives. Nucleic Acids Res.51, D654–D659. 10.1093/nar/gkac1008
34
Garcia-PerezI.PosmaJ. M.Serrano-ContrerasJ. I.BoulangeC. L.ChanQ.FrostG.et al (2020). Identifying unknown metabolites using NMR-based metabolic profiling techniques. Nat. Protoc.15, 2538–2567. 10.1038/s41596-020-0343-3
35
GaultonA.HerseyA.NowotkaM.BentoA. P.ChambersJ.MendezD. (2017). The ChEMBL database in 2017. Nucleic Acids Res.45, D945–D954.
36
GentileF.AgrawalV.HsingM.TonA. T.BanF.NorinderU.et al (2020). Deep docking: A deep learning platform for augmentation of structure based drug discovery. ACS Cent. Sci.6, 939–949. 10.1021/acscentsci.0c00229
37
GhislatG.RahmanT.BallesterP. J. (2021). Recent progress on the prospective application of machine learning to structure-based virtual screening. Curr. Opin. Chem. Biol.65, 28–34. 10.1016/j.cbpa.2021.04.009
38
GiavaliscoP.HummelJ.LisecJ.InostrozaA. C.CatchpoleG.WillmitzerL. (2008). High-resolution direct infusion-based mass spectrometry in combination with whole 13C metabolome isotope labeling allows unambiguous assignment of chemical sum formulas. Anal. Chem.80, 9417–9425. 10.1021/ac8014627
39
Gomez-GarciaA.Medina-FrancoJ. L. (2022). Progress and impact of Latin American natural product databases. Biomolecules12, 1202. 10.3390/biom12091202
40
GorgullaC.BoeszoermenyiA.WangZ. F.FischerP. D.CooteP. W.Padmanabha DasK. M.et al (2020). An open-source drug discovery platform enables ultra-large virtual screens. Nature580, 663–668. 10.1038/s41586-020-2117-z
41
GorgullaC.JayarajA.FackeldeyK.ArthanariH. (2022). Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches. Curr. Opin. Chem. Biol.69, 102156. 10.1016/j.cbpa.2022.102156
42
GrigalunasM.BrakmannS.WaldmannH. (2022). Chemical evolution of natural product structure. J. Am. Chem. Soc.144, 3314–3329. 10.1021/jacs.1c11270
43
GrigalunasM.BurhopA.ChristoforowA.WaldmannH. (2020). Pseudo-natural products and natural product-inspired methods in chemical biology and drug discovery. Curr. Opin. Chem. Biol.56, 111–118. 10.1016/j.cbpa.2019.10.005
44
GrygorenkoO. O.RadchenkoD. S.DziubaI.ChuprinaA.GubinaK. E.MorozY. S. (2020). Generating multibillion chemical space of readily accessible screening compounds. iScience23, 101681. 10.1016/j.isci.2020.101681
45
GuijasC.Montenegro-BurkeJ. R.Domingo-AlmenaraX.PalermoA.WarthB.HermannG.et al (2018). Metlin: A technology platform for identifying knowns and unknowns. Anal. Chem.90, 3156–3164. 10.1021/acs.analchem.7b04424
46
JumperJ.EvansR.PritzelA.GreenT.FigurnovM.RonnebergerO.et al (2021). Highly accurate protein structure prediction with AlphaFold. Nature596, 583–589. 10.1038/s41586-021-03819-2
47
KarageorgisG.FoleyD. J.LaraiaL.WaldmannH. (2020). Principle and design of pseudo-natural products. Nat. Chem.12, 227–235. 10.1038/s41557-019-0411-x
48
KasapC.ElementoO.KapoorT. M. (2014). DrugTargetSeqR: A genomics- and CRISPR-cas9-based method to analyze drug targets. Nat. Chem. Biol.10, 626–628. 10.1038/nchembio.1551
49
KoehnF. E.CarterG. T. (2005). The evolving role of natural products in drug discovery. Nat. Rev. Drug Discov.4, 206–220. 10.1038/nrd1657
50
KuritaK. L.GlasseyE.LiningtonR. G. (2015). Integration of high-content screening and untargeted metabolomics for comprehensive functional annotation of natural product libraries. Proc. Natl. Acad. Sci. U. S. A.112, 11999–12004. 10.1073/pnas.1507743112
51
LlanosM. A.AlbercaL. N.RuizM. D.SbaragliniM. L.MirandaC.Pino-MartinezA.et al (2023). A combined ligand and target-based virtual screening strategy to repurpose drugs as putrescine uptake inhibitors with trypanocidal activity. J. Comput. Aided Mol. Des.37, 75–90. 10.1007/s10822-022-00491-0
52
LuiG.GuaraldiG. (2023). Drug treatment of COVID-19 infection. Curr. Opin. Pulm. Med.29, 174–183. 10.1097/MCP.0000000000000953
53
LyuJ.WangS.BaliusT. E.SinghI.LevitA.MorozY. S.et al (2019). Ultra-large library docking for discovering new chemotypes. Nature566, 224–229. 10.1038/s41586-019-0917-9
54
MacheleidtJ.MatternD. J.FischerJ.NetzkerT.WeberJ.SchroeckhV.et al (2016). Regulation and role of fungal secondary metabolites. Annu. Rev. Genet.50, 371–392. 10.1146/annurev-genet-120215-035203
55
McfedriesA.SchwaidA.SaghatelianA. (2013). Methods for the elucidation of protein-small molecule interactions. Chem. Biol.20, 667–673. 10.1016/j.chembiol.2013.04.008
56
Medina-FrancoJ. L. (2020). Towards a unified Latin American natural products database: LANaPD. Future Sci. OA6, FSO468. 10.2144/fsoa-2020-0068
57
MotikaS. E.HergenrotherP. J. (2020). Re-engineering natural products to engage new biological targets. Nat. Prod. Rep.37, 1395–1403. 10.1039/d0np00059k
58
MullerC.RabalO.Diaz GonzalezC. (2022). Artificial intelligence, machine learning, and deep learning in real-life drug design cases. Methods Mol. Biol.2390, 383–407. 10.1007/978-1-0716-1787-8_16
59
NajmiA.JavedS. A.Al BrattyM.AlhazmiH. A. (2022). Modern approaches in the discovery and development of plant-based natural products and their analogues as potential therapeutic agents. Molecules27, 349. 10.3390/molecules27020349
60
NunezM. J.Diaz-EufracioB. I.Medina-FrancoJ. L.OlmedoD. A. (2021). Latin American databases of natural products: Biodiversity and drug discovery against SARS-CoV-2. RSC Adv.11, 16051–16064. 10.1039/d1ra01507a
61
OlmedoD. A.Gonzalez-MedinaM.GuptaM. P.Medina-FrancoJ. L. (2017). Cheminformatic characterization of natural products from Panama. Mol. Divers21, 779–789. 10.1007/s11030-017-9781-4
62
PanR.BaiX.ChenJ.ZhangH.WangH. (2019). Exploring structural diversity of microbe secondary metabolites using osmac strategy: A literature review. Front. Microbiol.10, 294. 10.3389/fmicb.2019.00294
63
Peralta-MorenoM. N.Anton-MuñozV.Ortega-AlarconD.Jimenez-AlesancoA.VegaS.AbianO.et al (2023). Autochthonous Peruvian natural plants as potential SARS-CoV-2 mpro main protease inhibitors. Pharm. (Basel)16, 585. 10.3390/ph16040585
64
PillayL. C.NekatiL.MakhwitineP. J.NdlovuS. I. (2022). Epigenetic activation of silent biosynthetic gene clusters in endophytic fungi using small molecular modifiers. Front. Microbiol.13, 815008. 10.3389/fmicb.2022.815008
65
PyeC. R.BertinM. J.LokeyR. S.GerwickW. H.LiningtonR. G. (2017). Retrospective analysis of natural products provides insights for future discovery trends. Proc. Natl. Acad. Sci. U. S. A.114, 5601–5606. 10.1073/pnas.1614680114
66
RavenP. H.GereauR. E.PhillipsonP. B.ChatelainC.JenkinsC. N.Ulloa UlloaC. (2020). The distribution of biodiversity richness in the tropics. Sci. Adv.6, eabc6228. 10.1126/sciadv.abc6228
67
RodriguesR. P.ArdissonJ. S.Ribeiro GoncalvesR. C.OliveiraT. B.Barreto Da SilvaV.KawanoD. F.et al (2019). Search for potential inducible nitric oxide synthase inhibitors with favorable ADMET profiles for the therapy of Helicobacter pylori infections. Curr. Top. Med. Chem.19, 2795–2804. 10.2174/1568026619666191112105650
68
SadybekovA. A.SadybekovA. V.LiuY.Iliopoulos-TsoutsouvasC.HuangX. P.PickettJ.et al (2022). Synthon-based ligand discovery in virtual libraries of over 11 billion compounds. Nature601, 452–459. 10.1038/s41586-021-04220-9
69
Sánchez-CruzN.Pilón-JiméneB. A.Medina-FrancJ. L. (2020). Functional group and diversity analysis of biofacquim: A Mexican natural product database. F1000Research8, 2071. 10.12688/f1000research.21540.2
70
SandulescuO.ApostolescuC. G.PreotescuL. L.Streinu-CercelA.SandulescuM. (2023). Therapeutic developments for SARS-CoV-2 infection-Molecular mechanisms of action of antivirals and strategies for mitigating resistance in emerging variants in clinical practice. Front. Microbiol.14, 1132501. 10.3389/fmicb.2023.1132501
71
SarkarC.DasB.RawatV. S.WahlangJ. B.NongpiurA.TiewsohI.et al (2023). Artificial intelligence and machine learning technology driven modern drug discovery and development. Int. J. Mol. Sci.24, 2026. 10.3390/ijms24032026
72
SchreiberS. L. (2009). Organic chemistry: Molecular diversity by design. Nature457, 153–154. 10.1038/457153a
73
SelickH. E.BeresfordA. P.TarbitM. H. (2002). The emerging importance of predictive ADME simulation in drug discovery. Drug Discov. Today7, 109–116. 10.1016/s1359-6446(01)02100-6
74
SettenR. L.RossiJ. J.HanS. P. (2019). The current state and future directions of RNAi-based therapeutics. Nat. Rev. Drug Discov.18, 421–446. 10.1038/s41573-019-0017-4
75
ShakerB.AhmadS.LeeJ.JungC.NaD. (2021). In silico methods and tools for drug discovery. Comput. Biol. Med.137, 104851. 10.1016/j.compbiomed.2021.104851
76
SimJ.KwonS.SeokC. (2023). HProteome-BSite: Predicted binding sites and ligands in human 3D proteome. Nucleic Acids Res.51, D403–D408. 10.1093/nar/gkac873
77
SinghN.ChaputL.VilloutreixB. O. (2021). Virtual screening web servers: Designing chemical probes and drug candidates in the cyberspace. Brief. Bioinform22, 1790–1818. 10.1093/bib/bbaa034
78
SorokinaM.MerseburgerP.RajanK.YirikM. A.SteinbeckC. (2021). COCONUT online: Collection of open natural products database. J. Cheminform13, 2. 10.1186/s13321-020-00478-9
79
StavrianidiA. (2020). A classification of liquid chromatography mass spectrometry techniques for evaluation of chemical composition and quality control of traditional medicines. J. Chromatogr. A1609, 460501. 10.1016/j.chroma.2019.460501
80
SubramanianA.NarayanR.CorselloS. M.PeckD. D.NatoliT. E.LuX.et al (2017). A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell171, 1437–1452. 10.1016/j.cell.2017.10.049
81
ThomasM.BenderA.De GraafC. (2023). Integrating structure-based approaches in generative molecular design. Curr. Opin. Struct. Biol.79, 102559. 10.1016/j.sbi.2023.102559
82
Valera-VeraE.ReigadaC.SayeM.DigirolamoF. A.GalceranF.MirandaM. R.et al (2022). Trypanocidal activity of the anthocyanidin delphinidin, a non-competitive inhibitor of arginine kinase. Nat. Prod. Res.36, 3153–3157. 10.1080/14786419.2021.1947270
83
Van HattumH.WaldmannH. (2014). Biology-oriented synthesis: Harnessing the power of evolution. J. Am. Chem. Soc.136, 11853–11859. 10.1021/ja505861d
84
VargasE. L. G.De AlmeidaF. A.De FreitasL. L.PintoU. M.VanettiM. C. D. (2021). Plant compounds and nonsteroidal anti-inflammatory drugs interfere with quorum sensing in Chromobacterium violaceum. Arch. Microbiol.203, 5491–5507. 10.1007/s00203-021-02518-w
85
WangM.CarverJ. J.PhelanV. V.SanchezL. M.GargN.PengY.et al (2016a). Sharing and community curation of mass spectrometry data with global natural products social molecular networking. Nat. Biotechnol.34, 828–837. 10.1038/nbt.3597
86
WangS.LinH.HuangZ.HeY.DengX.XuY.et al (2022). CavitySpace: A database of potential ligand binding sites in the human proteome. Biomolecules12, 967. 10.3390/biom12070967
87
WangY.CornettA.KingF. J.MaoY.NigschF.ParisC. G.et al (2016b). Evidence-based and quantitative prioritization of tool compounds in phenotypic drug discovery. Cell Chem. Biol.23, 862–874. 10.1016/j.chembiol.2016.05.016
88
WangY.XiaoJ.SuzekT. O.ZhangJ.WangJ.BryantS. H. (2009). PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res37, W623–W633.
89
WohlgemuthG.MehtaS. S.MejiaR. F.NeumannS.PedrosaD.PluskalT.et al (2016). SPLASH, a hashed identifier for mass spectra. Nat. Biotechnol.34, 1099–1101. 10.1038/nbt.3689
90
WolfenderJ. L.NuzillardJ. M.Van Der HooftJ. J. J.RenaultJ. H.BertrandS. (2019). Accelerating metabolite identification in natural product research: Toward an ideal combination of liquid chromatography-high-resolution tandem mass spectrometry and NMR profiling, in silico databases, and chemometrics. Anal. Chem.91, 704–742. 10.1021/acs.analchem.8b05112
91
WongF.KrishnanA.ZhengE. J.StarkH.MansonA. L.EarlA. M.et al (2022). Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery. Mol. Syst. Biol.18, e11081. 10.15252/msb.202211081
92
WuF.ZhouY.LiL.ShenX.ChenG.WangX.et al (2020). Computational approaches in preclinical studies on drug discovery and development. Front. Chem.8, 726. 10.3389/fchem.2020.00726
93
ZechaJ.BayerF. P.WiechmannS.WoortmanJ.BernerN.MullerJ.et al (2023). Decrypting drug actions and protein modifications by dose- and time-resolved proteomics. Science380, 93–101. 10.1126/science.ade3925
94
ZhouZ.XiongX.ZhuZ. J. (2017). MetCCS predictor: A web server for predicting collision cross-section values of metabolites in ion mobility-mass spectrometry based metabolomics. Bioinformatics33, 2235–2237. 10.1093/bioinformatics/btx140
95
ZieglerS.SieversS.WaldmannH. (2021). Morphological profiling of small molecules. Cell Chem. Biol.28, 300–319. 10.1016/j.chembiol.2021.02.012
Summary
Keywords
natural products, virtual screening, Latin America, databases, computation
Citation
Thomson TM (2023) On the importance for drug discovery of a transnational Latin American database of natural compound structures. Front. Pharmacol. 14:1207559. doi: 10.3389/fphar.2023.1207559
Received
17 April 2023
Accepted
15 June 2023
Published
22 June 2023
Volume
14 - 2023
Edited by
Carmenza Spadafora, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología, Panama
Reviewed by
Alan Hesketh, Independent Researcher, Gerrards Cross, United Kingdom
Abraham Madariaga-Mazon, National Autonomous University of Mexico, Mexico
Updates
Copyright
© 2023 Thomson.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Timothy M. Thomson, titbmc@ibmb.csic.es
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.