Open access peer-reviewed chapter - ONLINE FIRST

High-Throughput Screening for Drug Discovery toward Infectious Diseases: Options and Challenges

Written By

Ankur Gupta, Swatantra Kumar, Vimal K. Maurya, Bipin Puri and Shailendra K. Saxena

Submitted: August 17th, 2021Reviewed: January 28th, 2022Published: March 15th, 2022

DOI: 10.5772/intechopen.102936

High-Throughput Screening for Drug DiscoveryEdited by Shailendra K. Saxena

From the Edited Volume

High-Throughput Screening for Drug Discovery [Working Title]

Prof. Shailendra K. Saxena

Chapter metrics overview

34 Chapter Downloads

View Full Metrics


The increase in the number of antibiotic-resistant microbial strains makes it evident to discover and develop newer efficacious anti-infective drugs. High-throughput screening (HTS) is a robust technology that plays a crucial role in identifying novel anti-infective lead compounds. This chapter briefly explains the role of virtual HTS (vHTS) and HTS technologies in lead identification using various categories of chemical libraries through structure-based drug design, ligand-based drug design, in vitro cell-based assay, and biochemical assay approaches involved in the process of drug design and discovery. The chapter also gives an insightful survey of the technologies such as fluorescence, luminescence, and atomic absorbance used for the detection of biological responses in the HTS bioassays. Applications of HTS, reverse pharmacology, current challenges, and future perspectives of HTS in the pharmaceutical and biotechnology industry are discussed in the context of anti-infective drug design, discovery, and development.


  • HTS
  • vHTS
  • bioassay
  • anti-infective
  • drug discovery

1. Introduction

Antibiotic resistance among evolving microbes has been a matter of concern for pharmaceutical and biotechnology companies around the globe. These emerging pathogens with multiple drug resistance capabilities necessitate the discovery and development of both novel targets and anti-infective drugs. The key to success in anti-infective drug discovery depends on the identification of the target (a novel target for existing or novel strain of microbe) and a substantially active lead molecule against the designated target. Among the two key steps, the former requires genome sequence analysis (genomics) and protein expression analysis (proteomics) to identify target genes/proteins for a broad variety of microbial pathways and the latter requires chemical library screening against the defined target [1]. The chemical library may be generated through various routes such as combinatorial chemistry, bioassay-guided isolation of natural products, food and drug administration (FDA)-approved drugs for repurposing, virtually designed chemical library based on structure-based drug design (SBDD) or quantitative structure-activity relationship (QSAR), or other chemicals for fragment-based drug design (FBDD). However, with the latest advancement in technology, lead identification can be performed using high-throughput screening (HTS). HTS is a highly efficient automated method of screening chemical libraries to identify the so-called “hits,” which are further modified to drug “leads” for lead optimization through medicinal chemistry approaches [2]. Generally, HTS involves biological or biochemical assay screening [3], whereas computer-based chemical library screening for “hits” identification is termed virtual high-throughput screening (vHTS). However, both the methods are used simultaneously or in parallel enabling the scientists to think computationally, act chemically, and observe biologically [4, 5]; therefore, in the present chapter, vHTS has been coupled with HTS for ease of understanding the correlation between all the stages of the drug discovery process (Figure 1).

Figure 1.

Drug discovery process.


2. HTS in drug discovery for infectious diseases

Infectious diseases arise in any person due to certain microbes which enter the body and multiply to give clinical symptoms of the disease. While some infections are contagious and spread from one person to another person, others may spread in the community through infectious vectors (insect/animal bites) or contaminated air, water, and food [6]. These microbes undergo mechanisms of resistance to antibiotics either under the direct influence of antibiotics or through adaptive processes unrelated to the chemical structures of antibiotics [7, 8]. The increase in the number of antibiotic-resistant microbial strains makes it evident to discover and develop newer efficacious drugs. However, developing a new drug is a tedious and complex process with uncertain outcomes; therefore, the process needs to be rational in approach. HTS offers a highly rationalized automation approach to explore large chemical space in a time-efficient manner. However, it requires complex and costly technological platforms which are generally available in pharmaceutical companies [4]. Nevertheless, it is not expensive because HTS screens a huge number of chemical compounds as compared to manual methods for target-to-lead discovery. An overall success rate of HTS to find leads is considered ⁓50%. However, vHTS is considered to have a higher success rate, but every method has its strengths and weaknesses, and therefore both the methods, HTS and vHTS, should be coupled for lead discovery. Few examples, among successful HTS drug discoveries in anti-infective agents, are (i) G-protein-coupled receptor (GPCR) inhibitor, Maraviroc (anti-HIV), (ii) reverse transcriptase (RT) inhibitor, etravirine (anti-HIV), [9] and (iii) hepatitis C virus (HCV) genotype 1a/b or 3 RNA replication inhibitor, Daclatasvir [10].

2.1 Need of HTS in drug discovery for infectious diseases

Bacterial enzymes play a significant role in developing antibiotic resistance through several key mechanisms and genetically derived mutations happening in: (i) drug-modifying enzymes (such as transferasesand hydrolases), (ii) drug-metabolizing enzymes (such as pyrazinamidase, catalase-peroxidase, and monooxygenase), (iii) antibiotic’s target enzymes (such as RNA-dependent RNA polymerase(RdRp) and Topoisomerase II), and (iv) antibiotic’s cellular target-modifying enzymes (rRNAmethyltransferasesand phosphoethanolaminetransferase). The structural changes in these enzymes not only lead to resistance among microbes but also open the ‘omics gates to identify newer targets that originated after modifications in enzymes [8, 9, 10]. The rapid spread of resistance among microbes makes it imperative to rapidly identify new classes of antibiotics. Traditionally, growth inhibition assays are used for antimicrobial drug discovery which is a slow process [11, 12]. However, to match the pace of microbial resistance to antibiotics, a robotic automation screening process with efficient, accurate, and robust scientific methodology is required. HTS offers an economic advantage of screening huge chemical spaces accurately within defined timelines. Therefore, time, cost, and quality are termed as the “magic triangle of HTS” [13]. The credit of rapid HTS goes to: (i) high-density arrays, micro-reaction wells, and (ii) biological response detection methods.

High-density array micro-reaction well plates ranging from 96-well plates to miniaturized 3456-well plates are available with typical working volumes ranging from 1 to 10 μl of total volume. However, efforts are being made for further miniaturization of plates [13] to develop mega-dense arrays (>10,000 wells/plate) [14]. Although there are few difficulties associated with ultra-high-density plates, nevertheless, it is possible to perform 100,000 assays per day using ultra-high-throughput screening (uHTS) [15].

Biological response detection techniques such as fluorescence, luminescence, and atomic absorption spectroscopy have been established, which makes the process robust in the identification of active compounds. These direct and indirect detection methods have been developed based on: (i) direct measurement of absorbance and (ii) indirect measurement through enzymatic or chemical reactions coupled with pH indicators and chelators. These methods establish a quantitative relationship between biological response and target metabolite concentration [16]. Apart from the pharmacological aspect, HTS is equally beneficial in the evaluation of toxicological aspects of the chemical entities such as (i) genotoxicity, (ii) carcinogenicity, and (iii) immunotoxicity [15].

Plants have an abundance of potentially diverse chemical entities in the form of complex mixtures which are required to be evaluated in HTS for the discovery of new drugs against microbes. However, pure chemical entities from these complex mixtures need to be isolated and structurally characterized before proceeding for target-specific evaluation. Bioassay (in vitro)-guided HTS of these plant extracts aids in the identification and isolation of bioactive compounds (Figure 1) [17].

Similarly, vHTS is a bioethical approach consisting of a wide variety of in silicosimulation approaches to explore chemical libraries and identify which chemical entity has the potential to display in vitroand/or in vivodrug-like properties in HTS. However, there are chances of false-negative and false-positive results [4].


3. Methods involved in HTS for drug discovery toward infectious diseases

3.1 Classification of HTS

HTS methods for anti-infective drug discovery may be biological (cell-based or whole organism), biochemical (enzymes/receptors), and virtual (computer-based). Hence, the HTS methods may be classified as summarized in Figure 2. The HTS assay approach for the identification of “lead” molecule may vary depending on the target; however, the assay protocol must be (i) sensitive to low potency molecules, (ii) reproducible in biological response, (iii) accurate in terms of positive and negative control, and (iv) economically feasible. Therefore, these parameters should be optimized before proceeding with the assay of compounds in large numbers [18].

Figure 2.

Classification of HTS for anti-infective drug discovery.

3.1.1 Virtual high-throughput screening (computer simulation-guided selection)

vHTS is an efficient approach to identify hits and lead compounds for an identified microbial target which are further optimized using medicinal chemistry approaches. The applications of vHTS can be further explored to virtually evaluate ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the identified lead chemical entities based on “Lipinski’s rule.” The shortlisted potential hit/lead molecules may then be evaluated in vitro,thus giving a meaningful rationale between computer simulations and practical experimentation. Where vHTS is a generalized term for different screening filters, it is categorized under two broad classes of virtual screening methods. These methods are (i) structure-based drug design (SBDD) and ligand-based drug design (LBDD) [19]. Structure-based drug design

Advances in HT ‘omics technologies and instrumental methods of analysis such as X-ray crystallography and nuclear magnetic resonance (NMR) have solved a large number of three-dimensional (3D) structures of target proteins involved in communicable and noncommunicable diseases. These structures with specific identification numbers and resolution are available for scientific research and education purposes in protein data bank (PDB) [20]. Therefore, understanding the biologically functional interacting pocket (druggable target site) within 3D structures of the target proteins is essential to proceed with SBDD. However, if this structural information is not completely reliable or any sequence of the structural information is missing, then homology modeling is performed to generate a homologous model of the target protein [21]. SBDD is further classified under two headings; (i) docking and scoring and (ii) de novodrug design.

Docking and scoring is an excellent approach to predict the binding affinity and pharmacodynamic status of small chemical entities (ligands) in the active site of the target macromolecule. Scoring is an energy function which estimates the free binding energy of protein-ligand interactions such as electrostatic and van der Waals forces. Docking may be performed using two theoretical strategies namely: (i) lock and key theory, and (ii) induced-fit theory. Earlier docking programs were run using the lock and key assumptions where both the target protein and the ligand were treated as rigid structures with docking affinity dependent on the shape of the interacting structures. Hence, it is termed as a rigid docking program. However, the target proteins and ligands are never in their rigid conformational state; instead, they are flexible (induced-fit docking) and undertake complementary conformational changes. Therefore, optimizing the binding pocket enables it to accommodate ligands of various shapes and sizes. This approach reduces the dropping out chances of potential false negatives [22, 23].

De novodesign is a method of drug design that involves six different strategies: (i) identifying site point within the target site and connecting them using chemical fragments, (ii) determination of desirable fragment location, (iii) positioning fragment within the target site and linking them with linkers or scaffolds, (iv) construction of ligand sequentially within the site using fragments, (v) whole molecule conformation and interaction studies similar to docking, and (vi) random connection methods [24]. Ligand-based drug design

LBDD approach is applicable when nothing is known about the 3D structure of the target site and completely relies on the knowledge of previously established lead/drug molecules with known pharmacological/toxicological profiles and 2D/3D physicochemical descriptors. Therefore, LBDD is classified into two broad categories: (i) quantitative structure–activity relationship (QSAR) [25] and (ii) pharmacophore modeling [26]. However, scaffold hopping [27] and pseudo-receptor modeling [28] are also the strategies used in LBDD.

QSAR is a method for developing mathematical models to significantly correlate the pharmacological profile with the chemical structures within the data set using regression analysis. However, with technological advancement, the QSAR method has undergone dimensional transformations (2D and 3D). The process involves a collection of chemical data sets (in-house or external) to develop mathematical QSAR models. These models are then used to identify active compounds which are sequentially evaluated and synthesized on various platforms, including docking, in vitro,and in vivostudies [25].

Scaffold hopping is also known as “lead hopping” as it starts with known active compounds which are modified using 1–4° chemical replacement in the known lead structure to generate a novel chemotype which is further evaluated using various platforms, including docking, in vitro, and in vivostudies [27]. In contrast, pseudo-receptor design is a method closely related to homology modeling of SBDD where presumed bioactive conformations of overlaid molecules are used to generate the target’s pseudo-binding site map for further SBDD. Hence, this method is a bridge between LBDD and SBDD [28].

Pharmacophore fingerprinting is a method to identify a common “pharmacophore feature” among a set of active drug or lead molecules that may be used in SBDD and/or LBDD. The pharmacophore feature is an essential chemical portion of lead/drug molecules which is required for biological functions and may include hydrogen bond donors/acceptors, aromatic rings, hydrophilic/hydrophobic attachments, or any possible combinations. These features are enumerated in terms of three-point and four-point sets of varied pharmacophores to measure the distance in terms of bonds. Pharmacophore fingerprints thus generated are utilized for developing novel lead molecules in combination with SBDD (Figure 3) [26].

Figure 3.

Ligand and structure-based drug designing process.

3.1.2 High-throughput screening (bioassay-guided selection) Cell-based assays

Various unexplored targets and pathways lie within the components of cellular complexity which offers an excellent platform to identify antimicrobial lead molecules through the cell (or organism)-based HTS. Thus, multiple targets can be screened using cell-based assays in all the stages of drug discovery. In simple words, these assays are used when the desired cellular target is either unknown or the phenotype cannot be separated from the cellular context. Nevertheless, these assays provide additional information which cannot be obtained from biochemical assays or vHTS, such as membrane permeability, pharmacodynamic (agonist, partial agonist, inverse agonist, and antagonist) status, cell proliferation (or viability), cytotoxicity, heterogeneity, protein expression, transcriptional readouts, and phenotypic biomarker readouts. Thus, cell-based assays may be classified depending on the methodologies used such as: (i) cell viability assays using (a.) dyes like Alamar blue, tetrazolium compounds (MTT assay, XTT assay, and MTS assay) which get converted to generate fluorescence or color indicating cell death or viability; (b.) luciferin-luciferase assay where ATP content is measured using luciferin-luciferase to generate bioluminescence; (c.) intercalation with membrane-permeant DNA dyes; (ii) reporter gene assay; (iii) secondary messenger assay; (iv) protein-fragment complementation assay; (v) protein–protein interaction assay; (vi) label-free methods; and (vii) phenotype biomarker assays. For anti-infective drug discovery, cell viability assays with different cell lines are utilized to screen and identify molecules that can kill or inhibit the growth of pathogens. These assays are further utilized to evaluate the safety issues of the organs such as the liver because the liver is the primary center for drug metabolism [29]. Biochemical assays

Biochemical assays involve screening of chemical libraries for in vitroinhibition of purified target protein (enzyme, receptor, and ion channels) in competition format where the known substrate bound to protein is replaced by the ligand or compound under study. The biological response is detected using optical methods such as fluorescence, luminescence, or absorbance [29].

3.2 Biological response detection methods in HTS

The detection of biological response in the cell-based and/or biochemical assay may be performed using different analytical technologies such as, fluorescence-based assays [FRET, HTRF, dissociation-enhanced lanthanide fluorescent immunoassay (DELFIA), time-resolved FRET (TR-FRET), fluorescence polarization (FP), fluorescent lifetime (FLT)], luminescence-based assays [bioluminescence resonance energy transfer (BRET), amplified luminescent proximity homogeneous assay (ALPHA), electrochemiluminescence assay (ECL)], atomic absorption spectroscopy (AAS), high-throughput electrophysiology (HT electrophysiology), protein complementation assay (PCA), Scintillation proximity assay (SPA), and enzyme fragment complementation (EFC) [18], which are further modified with different variations. However, the detailed discussion on the usage of these variations in the design of HTS assays is beyond the scope of this chapter.


4. Applications and outcomes of HTS in anti-infective drug discovery

HTS is being applied in a myriad of ways starting from the biology of infectious diseases to finding the lead molecules for anti-infective drug discovery. Few applications of HTS in infectious biology are the identification of pathogenic molecular mechanisms, evolutionary analysis of pathogens, and determination of the determinants required for survival and pathogenesis of the mutant strains of the microbial population [30]. Although, HTS is an early-stage drug development program, however, the anti-infective drug discovery efforts with HTS from the year 2000 to date have led to the approval of 38 new antibacterial drugs and 67 drug candidates are in the clinical development stage for both Gram-negative and Gram-positive bacteria including Mycobacterium tuberculosis. Nevertheless, 19 different compounds with novel pharmacophore are in different stages of clinical development (6 compounds in Phase I, 9 compounds in Phase II, 4 compounds in Phase III) [31].


5. Reverse pharmacology in drug discovery for infectious diseases

HTS bioassay-guided identification and isolation of bioactive compounds from natural biodiversity is termed as “Reverse Pharmacognosy.” Similarly, isolating a chemical entity and developing a pharmaceutical product from the clinically proven herbal remedy is termed “Reverse Pharmacology.” Quinine and Artemisinin are the two well-known antimalarial lead molecules identified and isolated through this approach [32] which were optimized using HTS and chemistry approaches to various antimalarial drugs with a better pharmacokinetic and pharmacodynamic profile.


6. Challenges in drug discovery for infectious diseases

The major challenge in drug discovery for infectious diseases is the mutation in superbugs which make them evolve rapidly. Despite the availability of structural information of 62,206 bacterially derived proteins in PDB, mutational changes in these structures necessitate continuous research in ‘omics studies. Moreover, the virus-derived proteins are only 9603 in number [20] leading to a reduced success rate of structure-based design of antiviral drugs. Nevertheless, many QSAR projects fail at the model building stage due to a lack of interdisciplinary application during the execution of the project. Similarly, a considerable challenge at the stage of in vitro/in vivoscreening is the penetration of molecules into the bacterial cell, especially in Gram-negative species. However, these challenges may be countered with a diversified chemical space which is again a challenge for combinatorial chemistry-based chemical libraries. Therefore, biodiversity needs to be explored for identifying novel pharmacophores and associated anti-infective drugs.


7. Future perspectives

Highly diversified chemical space is a must for identifying novel pharmacophores which can be obtained through engineering biodiversity. Phytochemical hybridization [33] and phytochemical engineering [34] offer a great advantage to generate diverse semisynthetic chemical libraries which may be fruitful in identifying novel anti-infective pharmacophores. Further, nanotechnology is an emerging technology through which nanoprobes may be utilized to analyze microbes. Hence, HTS incorporated with nanotechnology may improve the efficiency of HTS [16]. Similarly, microfluidic technology may enable the use of a single platform to combine genome sequencing, mining, and uHTS. Thus, this technology may open up unique opportunities for anti-infective drug discovery at the level of single cell [35]. Further, given the urgency of the coronavirus (CoV) outbreak, HTS methodology using two types of mild CoV, HCoV-OC43 and MHV, was developed as a valuable tool for the rapid identification of promising drugs against CoV without the drawbacks of level three biological confinements. The luciferase reporter gene is introduced into HCoV-OC43 and MHV to indicate viral activity, and hence the antiviral efficiency of screened drugs can be quantified by luciferase activity. Compounds with antiviral activity against both HCoV-OC43 and MHV are further evaluated in SARS-CoV-2 after structural optimizations. This system allows large-scale compounds to be screened to search for broad spectrum drugs against CoV in a high-throughput manner, providing potential alternatives for clinical management of SARS-CoV-2 [36].


8. Conclusions

The goal of this chapter was to elucidate various options and platforms of the drug discovery process in correlation with anti-infective drugs. Where most of the discovery aspects starting from microbial resistance to target-to-lead identification through HTS strategies such as structure-based, ligand-based drug design, in vitrocell-based/biochemical assays, and biological response detection techniques are covered, the detailed explanation on each subtopic may be referred using the reference section. However, the challenges in anti-infective drug discovery remain a matter of concern for future research and development using different techniques to generate chemical space such as phytochemical hybridization and incorporation of nanotechnology in HTS for ultra-efficient screening and detection of biological response.



The authors are grateful to: the Director-General, Department of Health & Family Welfare, Government of Sikkim, India; the Principal, Government Pharmacy College, Sikkim, India; and the Vice-Chancellor, King George’s Medical University (KGMU), Lucknow, India, for the support and encouragement for this work.


Conflict of interest

The authors declare no conflict of interest.


  1. 1.Doostparast Torshizi A, Wang K. Next-generation sequencing in drug development: Target identification and genetically stratified clinical trials. Drug Discovery Today. 2018;23(10):1776-1783. DOI: 10.1016/j.drudis.2018.05.015
  2. 2.Payne DJ, Gwynn MN, Holmes DJ, Pompliano DL. Drugs for bad bugs: Confronting the challenges of antibacterial discovery. Nature Reviews. Drug Discovery. 2007;6(1):29-40. DOI: 10.1038/nrd2201
  3. 3.Aldewachi H, Al-Zidan RN, Conner MT, Salman MM. High-throughput screening platforms in the discovery of novel drugs for neurodegenerative diseases. Bioengineering (Basel). 2021;8(2):30. DOI: 10.3390/bioengineering8020030
  4. 4.Bellera CL, Sbaraglini ML, Talevi A. Modern approaches for the discovery of anti-infectious drugs for the treatment of neglected diseases. Current Topics in Medicinal Chemistry. 2018;18(5):369-381. DOI: 10.2174/1568026618666180509151146
  5. 5.Johnson EO, Hung DT. A point of inflection and reflection on systems chemical biology. ACS Chemical Biology. 2019;14(12):2497-2511. DOI: 10.1021/acschembio.9b00714
  6. 6.National Center for Emerging and Zoonotic Infectious Diseases. Available from:[Accessed: June 21, 2021].
  7. 7.Holmes AH, Moore LS, Sundsfjord A, et al. Understanding the mechanisms and drivers of antimicrobial resistance. Lancet. 2016;387(10014):176-187. DOI: 10.1016/S0140-6736(15)00473-0
  8. 8.Egorov AM, Ulyashova MM, Rubtsova MY. Bacterial enzymes and antibiotic resistance. ActaNaturae. 2018;10(4):33-48
  9. 9.Macarron R, Banks MN, Bojanic D, et al. Impact of high-throughput screening in biomedical research. Nature Reviews. Drug Discovery. 2011;10(3):188-195. DOI: 10.1038/nrd3368
  10. 10.Available from:[Accessed: June 21 2021]
  11. 11.Projan SJ, Shlaes DM. Antibacterial drug discovery: is it all downhill from here? Clinical Microbiology and Infection. 2004;4(Suppl. 10):18-22. DOI: 10.1111/j.1465-0691.2004.1006.x
  12. 12.Silver LL. A retrospective on the failures and successes of antibacterial drug discovery. IDrugs. 2005;8(8):651-655
  13. 13.Mayr LM, Fuerst P. The future of high-throughput screening. Journal of Biomolecular Screening. 2008;13(6):443-448. DOI: 10.1177/1087057108319644
  14. 14.Wingren C, Borrebaeck CA. Progress in miniaturization of protein arrays-a step closer to high-density nanoarrays. Drug Discovery Today. 2007;12(19-20):813-819. DOI: 10.1016/j.drudis.2007.08.003
  15. 15.Szymański P, Markowicz M, Mikiciuk-Olasik E. Adaptation of high-throughput screening in drug discovery-toxicological screening tests. International Journal of Molecular Sciences. 2012;13(1):427-452. DOI: 10.3390/ijms13010427
  16. 16.Zeng W, Guo L, Xu S, Chen J, Zhou J. High-throughput screening technology in industrial biotechnology. Trends in Biotechnology. 2020;38(8):888-906. DOI: 10.1016/j.tibtech.2020.01.001
  17. 17.Zhang HJ, Li WF, Fong HH, Soejarto DD. Discovery of bioactive compounds by the uic-icbg drug discovery program in the 18 years since 1998. Molecules. 2016;21(11):1448. DOI: 10.3390/molecules21111448
  18. 18.Inglese J, Johnson RL, Simeonov A, et al. High-throughput screening assays for the identification of chemical probes. Nature Chemical Biology. 2007;3(8):466-479. DOI: 10.1038/nchembio.2007.17
  19. 19.Aparoy P, Reddy KK, Reddanna P. Structure and ligand-based drug design strategies in the development of novel 5- LOX inhibitors. Current Medicinal Chemistry. 2012;19(22):3763-3778. DOI: 10.2174/092986712801661112
  20. 20.Available from: Protein data bank.[Accessed: June 21, 2021].
  21. 21.Muhammed MT, Aki-Yalcin E. Homology modeling in drug discovery: Overview, current applications, and future perspectives. Chemical Biology & Drug Design. 2019;93(1):12-20. DOI: 10.1111/cbdd.13388
  22. 22.Gazgalis D, Zaka M, Abbasi BH, Logothetis DE, Mezei M, Cui M. Protein binding pocket optimization for virtual high-throughput screening (vHTS) drug discovery. ACS Omega. 2020;5(24):14297-14307. DOI: 10.1021/acsomega.0c00522
  23. 23.Badrinarayan P, Sastry GN. Virtual high throughput screening in new lead identification. Combinatorial Chemistry & High Throughput Screening. 2011;14(10):840-860. DOI: 10.2174/138620711797537102
  24. 24.Murcko MA. In: Lipkowitz KB, Boyd DB, editors. Reviews in Computational Chemistry. Vol. 11. Hoboken, NJ, USA: John Wiley & Sons, Inc.; 2007. pp. 1-67
  25. 25.Neves BJ, Braga RC, Melo-Filho CC, Moreira-Filho JT, Muratov EN, Andrade CH. QSAR-based virtual screening: Advances and applications in drug discovery. Frontiers in Pharmacology. 2018;9:1275. DOI: 10.3389/fphar.2018.01275
  26. 26.Qing X, Lee XY, De Raeymaecker J, et al. Pharmacophoremodeling: advances, limitations, and current utility in drug discovery. Journal of Receptor, Ligand and Channel Research. 2014;7:81-92. DOI: 10.2147/JRLCR.S46843
  27. 27.Sun H, Tawa G, Wallqvist A. Classification of scaffold-hopping approaches. Drug Discovery Today. 2012;17(7-8):310-324. DOI: 10.1016/j.drudis.2011.10.024
  28. 28.Fayne D. Ligand-based molecular design using pseudoreceptors. In: Schneider G, editor. Chapter 9 De Novo Molecular Design. Germany: Wiley; 2013. pp. 227-244. DOI: 10.1002/9783527677016.ch9
  29. 29.Blay V, Tolani B, Ho SP, Arkin MR. High-throughput screening: Today’s biochemical and cell-based approaches. Drug Discovery Today. 2020;S1359-6446(20):30305-30306. DOI: 10.1016/j.drudis.2020.07.024
  30. 30.McAdam PR, Richardson EJ, Fitzgerald JR. High-throughput sequencing for the study of bacterial pathogen biology. Current Opinion in Microbiology. 2014;19(100):106-113. DOI: 10.1016/j.mib.2014.06.002
  31. 31.Butler MS, Paterson DL. Antibiotics in the clinical pipeline in October 2019. The Journal of Antibiotics (Tokyo). 2020;73(6):329-364. DOI: 10.1038/s41429-020-0291-8
  32. 32.Willcox ML, Graz B, Falquet J, Diakite C, Giani S, Diallo D. A reverse pharmacology approach for developing an anti-malarial phytomedicine. Malaria Journal. 2011;10(Suppl. 1):S8. DOI: 10.1186/1475-2875-10-S1-S8
  33. 33.Swain SS, Paidesetty SK, Padhy RN. Phytochemical conjugation as a potential semisynthetic approach toward reactive and reuse of obsolete sulfonamides against pathogenic bacteria. Drug Development Research. 2021;82(2):149-166. DOI: 10.1002/ddr.21746
  34. 34.Muhammad A, Feng X, Rasool A, Sun W, Li C. Production of plant natural products through engineered Yarrowialipolytica. Biotechnology Advances. 2020;43:107555. DOI: 10.1016/j.biotechadv.2020.107555
  35. 35.Terekhov SS, Osterman IA, Smirnov IV. High-throughput screening of biodiversity for antibiotic discovery. ActaNaturae. 2018;10(3):23-29
  36. 36.Liu J, Li K, Cheng L, Shao J, Yang S, Zhang W, et al. A high-throughput drug screening strategy against coronaviruses. International Journal of Infectious Diseases. 2021;103:300-304. DOI: 10.1016/j.ijid.2020.12.033

Written By

Ankur Gupta, Swatantra Kumar, Vimal K. Maurya, Bipin Puri and Shailendra K. Saxena

Submitted: August 17th, 2021Reviewed: January 28th, 2022Published: March 15th, 2022