Abstract
According to the WHO priority pathogens list, the six nosocomial pathogens that exhibit resistance to several antibiotics are named as ESKAPE, including Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter sp. Since most antibiotics are ineffective against ESKAPE group of microorganisms, it creates selective pressure on the clinical healthcare settings. Hence, it is imperative to design and develop novel therapeutic agents of synthetic and natural origin against these multidrug-resistant (MDR) bacteria. However, the limitations associated with conventional drug discovery pipelines have urged the scientific community to develop reverse pharmacology-based approaches to discover potential therapeutic drug candidates of interest. With the advancement of next generation computational approaches, i.e., computer-aided drug designing (CADD), quantitative structure affinity relationship (QSAR) studies, pharmacophore modelling, and pharmacokinetic profiling using ADMET (Absorption-Digestion-Metabolism-Excretion-Toxicity) predictions, the drug discovery pipelines received quintessential growth in last few years. In silico tools, particularly, molecular docking, MD Simulation, 3D-QSAR, ADMET profiling, etc. are being extensively explored in providing a platform for identifying and develo** potential therapeutic agents against several diseases of economic importance. The advanced computational tools and prediction software not only reduced the manpower, time, and selection of drug development processes but also created a favourable atmosphere to design and develop potential drug candidates against chronic microbial infections linked to ESKAPE pathogens. The in silico tools also provide an integrated platform for repurposing of therapeutic drugs toward the mitigation of bacterial infections and biofilms mediated resistance in bacterial pathogens. The present chapter emphasizes the use of advanced computational tools to identify potential drug candidates against ESKAPE pathogens. Besides, CADD-based approaches for FDA approved drugs with potential therapeutic values repurposed towards antibacterial and biofilm inhibition was also critically discussed. This chapter will thus provide new avenues to design and develop potential drug candidates with high throughput therapeutic values in the regulation of bacterial virulence and drug resistance patterns in ESKAPE pathogens in the near future.
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References
Abd El-Aleam RH, Sayed AM, Taha MN, George RF, Georgey HH, Abdel-Rahman HM (2022) Design and synthesis of novel benzimidazole derivatives as potential Pseudomonas aeruginosa anti-biofilm agents inhibiting LasR: evidence from comprehensive molecular dynamics simulation and in vitro investigation. Eur J Med Chem 241:114629. https://doi.org/10.1016/j.ejmech.2022.114629
Abinaya M, Gayathri M (2019) Inhibition of biofilm formation, quorum sensing activity and molecular docking study of isolated 3,5,7-Trihydroxyflavone from Alstonia scholaris leaf against P. aeruginosa. Bioorg Chem 87:291–301. https://doi.org/10.1016/j.bioorg.2019.03.050
Abishad P, Niveditha P, Unni V, Vergis J, Kurkure NV, Chaudhari S, Rawool DB, Barbuddhe SB (2021) In silico molecular docking and in vitro antimicrobial efficacy of phytochemicals against multi-drug-resistant enteroaggregative Escherichia coli and non-typhoidal Salmonella spp. Gut Pathog 13:46. https://doi.org/10.1186/s13099-021-00443-3
Aboagye CI, Ampomah GB, Mensah JO, Mensah CN, Nartey D, Gasu EN, Borquaye LS (2023) N-Benzylimidazoles as potential antimicrobial and antibiofilm agents—syntheses, bioactivity, and molecular docking studies. Sci Afr 19:e01529. https://doi.org/10.1016/j.sciaf.2022.e01529
Abo-Salem HM, Abd El Salam HA, Abdel-Aziem AM, Abdel-Aziz MS, El-Sawy ER (2021) Synthesis, molecular docking, and biofilm formation inhibitory activity of bis(indolyl)pyridines analogues of the marine alkaloid nortopsentin. Molecules 26(14):4112. https://doi.org/10.3390/molecules26144112
Adnan M, Patel M, Deshpande S, Alreshidi M, Siddiqui AJ, Reddy MN et al (2020) Effect of Adiantum philippense extract on biofilm formation, adhesion with its antibacterial activities against foodborne pathogens, and characterization of bioactive metabolites: an in vitro-in silico approach. Front Microbiol 11:823. https://doi.org/10.3389/fmicb.2020.00823
Agamah FE, Mazandu GK, Hassan R, Bope CD, Thomford NE, Ghansah A, Chimusa ER (2020) Computational/in silico methods in drug target and lead prediction. Brief Bioinform 21(5):1663–1675. https://doi.org/10.1093/bib/bbz103
Agüero-Chapin G, Galpert-Cañizares D, Dominguez-Perez D, Marrero-Ponce Y, Perez-Machado G, Teijeira M, Antunes A (2022) Emerging computational approaches for antimicrobial peptide discovery. Antibiotics. 11(7):936. https://doi.org/10.3390/antibiotics11070936
Ahmed F, Lee JW, Samantasinghar A, Kim YS, Kim KH, Kang IS et al (2022) SperoPredictor: an integrated machine learning and molecular docking-based drug repurposing framework with use case of COVID-19. Front Public Health 10:902123. https://doi.org/10.3389/fpubh.2022.902123
Almihyawi RAH, Naman ZT, Al-Hasani HMH, Muhseen ZT, Zhang S, Chen G (2022) Integrated computer-aided drug design and biophysical simulation approaches to determine natural anti-bacterial compounds for Acinetobacter baumannii. Sci Rep 12:6590. https://doi.org/10.1038/s41598-022-10364-z
Alves VM, Korn D, Pervitsky V, Thieme A, Capuzzi SJ, Baker N et al (2022) Knowledge-based approaches to drug discovery for rare diseases. Drug Discov Today 27(2):490–502. https://doi.org/10.1016/j.drudis.2021.10.014
Ambure P, Halder AK, Diaz HG, Cordeiro MNDS (2019) QSAR-Co: an open source software for develo** robust multitasking or multitarget classification-based QSAR models. J Chem Inf Model 59(6):2538–2544. https://doi.org/10.1021/acs.jcim.9b00295
Anju VT, Busi S, Mohan MS, Ranganathan S, Ampasala DR, Kumavath R, Dyavaiah M (2022) In vivo, in vitro and molecular docking studies reveal the anti-virulence property of hispidulin against Pseudomonas aeruginosa through the modulation of quorum sensing. Int Biodeterior Biodegradation 174:105487. https://doi.org/10.1016/j.ibiod.2022.105487
Ashburn TT, Thor KB (2004) Drug repositioning: identifying and develo** new uses for existing drugs. Nat Rev Drug Discov 3:673–683. https://doi.org/10.1038/nrd1468
Aswathanarayan JB, Vittal RR (2018) Inhibition of biofilm formation and quorum sensing mediated phenotypes by berberine in Pseudomonas aeruginosa and Salmonella typhimurium. RSC Adv 8:36133. https://doi.org/10.1039/c8ra06413j
Atanaki FF, Behrouzi S, Ariaeenejad S, Boroomand A, Kavousi K (2020) BIPEP: sequence-based prediction of biofilm inhibitory peptides using a combination of NMR and physicochemical descriptors. ACS Omega 5(13):7290–7297. https://doi.org/10.1021/acsomega.9b04119
Awadelkareem AM, Al-Shammari E, Elkhalifa AO, Adnan M, Siddiqui AJ, Mahmood D et al (2022) Anti-adhesion and antibiofilm activity of Eruca sativa Miller extract targeting cell adhesion proteins of food-borne bacteria as a potential mechanism: combined in vitro-in silico approach. Plants 11:610. https://doi.org/10.3390/plants11050610
Ayerbe-Algaba R, Gil-Marqués ML, Jiménez-MejÃas ME, Sánchez-Encinales V, Parra-Millán R, Pachón-Ibáñez ME, Pachón J, Smani Y (2018) Synergistic activity of niclosamide in combination with colistin against colistin-susceptible and colistin-resistant Acinetobacter baumannii and Klebsiella pneumoniae. Front Cell Infect Microbiol 8:348. https://doi.org/10.3389/fcimb.2018.00348
Bai Q, Liu S, Tian Y, Banegas-Luna AJ, Pérez-Sánchez H, Huang J, Liu H, Yao X (2022) Application advances of deep learning methods for de novo drug design and molecular dynamics simulation. WIREs Comput Mol Sci 12(3):e1581. https://doi.org/10.1002/wcms.1581
Baloyi IT, Adeosun IJ, Yusuf AA, Cosa S (2021) In silico and in vitro screening of antipathogenic properties of Melianthus comosus (Vahl) against Pseudomonas aeruginosa. Antibiotics 10:679. https://doi.org/10.3389/antibiotics10060679
Basak HK, Paswan U, Chatterjee A (2022) A computational approach to explore the binding mechanism of secondary metabolites of Hamigera ingelheimensis with AgrA quorum sensing proteins. Vietnam J Chem 60(1):21–36. https://doi.org/10.1002/vjch.202100046
Bassetti S, Tschudin-Sutter S, Egli A, Osthoff M (2022) Optimizing antibiotic therapies to reduce the risk of bacterial resistance. Eur J Intern Med 99:7–12. https://doi.org/10.1016/j.ejim.2022.01.029
Batool M, Ahmad B, Choi S (2019) A structure-based drug discovery paradigm. Int J Mol Sci 20:2783. https://doi.org/10.3390/ijms20112783
Behera SK, Panda AK, Mishra R, Mahanty A, Bisht SS (2022) Structure based virtual screening and molecular dynamics of natural anti-biofilm compounds against SagS response regulator/sensor kinase in Pseudomonas aeruginosa. J Biomol Struct Dyn 41:6011. https://doi.org/10.1080/07391102.2022.2100482
Bergstrom CAS, Larsson P (2018) Computational prediction of drug solubility in water-based systems: qualitative and quantitative approaches used in the current drug discovery and development setting. Int J Pharm 540:185–193. https://doi.org/10.1016/j.ijpharm.2018.01.044
Bhat AA, Tandon N, Singh I, Tandon R (2023) Structure-active relationship (SAR) and antibacterial activity of pyrrolidine based hybrids: a review. J Mol Struct 1283:135175. https://doi.org/10.1016/j.molstruc.2023.135175
Blaschke T, Arus-Porus J, Chen H, Margreitter C, Tyrchan C, Engkvist O et al (2020) REINVENT 2.0—an AI tool for de novo drug design. J Chem Inf Model 60(12):5918–5922. https://doi.org/10.1021/acs.jcim.0c00915
Boopathi S, Vashisth R, Manoharan P, Kandasamy R, Sivakumar N (2017) Stigmatellin Y—an anti-biofilm compound from Bacillus subtilis BR4 possibly interferes in PQS–PqsR mediated quorum sensing system in Pseudomonas aeruginosa. Bioorg Med Chem Lett 27:2113–2118. https://doi.org/10.1016/j.bmcl.2017.03.074
Breijyeh Z, Karaman R (2023) Design and synthesis of novel antimicrobial agents. Antibiotics 12:628. https://doi.org/10.3390/antibiotics12030628
Brogi S (2019) Computational approaches for drug discovery. Molecules 24(17):3061. https://doi.org/10.3390/molecules24173061
Cain R, Narramore S, McPhillie M, Simmons K, Fishwick CWG (2014) Applications of structure-based design to antibacterial drug discovery. Bioorg Chem 55:69–76. https://doi.org/10.1016/j.bioorg.2014.05.008
Cardoso MH, Orozco RQ, Rezende SB, Rodrigues G, Oshiro KGN, Candido ES, Franco OL (2020) Computer-aided design of antimicrobial peptides: are we generating effective drug candidates? Front Microbiol 10:3097. https://doi.org/10.3389/fmicb.2019.03097
Chaieb K, Kouidhi B, Hosawi SB, Baothman OAS, Zamzami MA, Altayeb HN (2022) Computational screening of natural compounds as putative quorum sensing inhibitors targeting drug resistance bacteria: molecular docking and molecular dynamics simulations. Comput Biol Med 145:105517. https://doi.org/10.1016/j.compbiomed.2022.105517
Chen W, Liu X, Zhang S, Chen S (2023) Artificial intelligence for drug discovery: resources, methods, and applications. Mol Ther Nucleic Acid 31:691–702. https://doi.org/10.1016/j.omtn.2023.02.019
Cordeiro L, Figueiredo P, Souza H, Sousa A, Andrade-Junior F, Medeiros D et al (2020a) Terpinen-4-ol as an antibacterial and antibiofilm agent against Staphylococcus aureus. Int J Mol Sci 21:4531. https://doi.org/10.3390/ijms21124531
Cordeiro L, Figueiredo P, Souza H, Sousa A, Andrade-Junior F, Barbosa-Filho J, Lima E (2020b) Antibacterial and antibiofilm activity of myrtenol against Staphylococcus aureus. Pharmaceuticals 13:133. https://doi.org/10.3390/ph13060133
Cortat Y, Nedyalkova M, Schindler K, Kadakia P, Demirci G, Sovari SN, Crochet A, Salentinig S, Lattuada M, Steiner OM, Zobi F (2023) Computer-aided drug design and synthesis of rhenium Clotrimazole antimicrobial agents. Antibiotics 12(3):619. https://doi.org/10.3390/antibiotics12030619
da Rosa TF, Coelho SS, Foletto VS, Bottega A, Serafin MB, de Souza Machado C, Franco LN, de Paula BR (2020) Alternatives for the treatment of infections caused by ESKAPE pathogens. J Clin Pharm Ther 45(4):863–873. https://doi.org/10.1111/jcpt.13149
da Silva TH, Hachigian TZ, Lee J, King MD (2022) Using computers to ESKAPE the antibiotic resistance crisis. Drug Discov Today 27(2):456–470. https://doi.org/10.1016/j.drudis.2021.10.005
Dadgostar P (2019) Antimicrobial resistance: implications and cost. Infect Drug Resist 12:3903–3910. https://doi.org/10.2147/IDR.S234610
Dai Q, Yan Y, Ning X, Li G, Yu J, Deng J et al (2021) AncPhore: a versatile tool for anchor pharmacophore steered drug discovery with applications in discovery of new inhibitors targeting metallo-β-lactamases and indoleamine/tryptophan 2,3-dioxygenases. Acta Pharm Sin B 11(7):1931–1946. https://doi.org/10.1016/j.apsb.2021.01.018
de Bruijn WJC, Hageman JA, Araya-Cloutier C, Gruppen H, Vincken JP (2018) QSAR of 1,4-benzoxazin-3-one antimicrobials and their drug design perspectives. Bioorg Med Chem 26(23–24):6105–6114. https://doi.org/10.1016/j.bmc.2018.11.016
de Macedo GHR, Costa GDE, Oliveira ER, Damasceno GV, Mendonca JSP, dos Santos SL et al (2021) Interplay between ESKAPE pathogens and immunity in skin infections: an overview of the major determinants of virulence and antibiotic resistance. Pathogens 10(2):148. https://doi.org/10.3390/pathogens10020148
De Oliveira DMP, Forde BM, Kidd TJ, Harris PNA, Schembri MA, Beatson SA, Paterson DL, Walker MJ (2020) Antimicrobial resistance in ESKAPE pathogens. Clin Microbiol Rev 33(3):e00181-19. https://doi.org/10.1128/CMR.00181-19
Deb R, Kabelka I, Pribyi J, Vacha R (2023) De novo design of peptides that form transmembrane barrel pores killing antibiotic resistant bacteria. Biophys J 122(3):155a. https://doi.org/10.1016/j.bpj.2022.11.984
Denissen J, Reyneke B, Waso-Reyneke M, Havenga B, Barnard T, Khan S, Khan W (2022) Prevalence of ESKAPE pathogens in the environment: antibiotic resistance status, community-acquired infection and risk to human health. Int J Hyg Environ Health 244:114006. https://doi.org/10.1016/j.ijheh.2022.114006
Desouky SE, Abu-Elghait M, Fayed EA, Selim S, Yousuf B, Igarashi Y et al (2022) Secondary metabolites of Actinomycetales as potent quorum sensing inhibitors targeting Gram-positive pathogens: in vitro and in silico study. Metabolites 12:246. https://doi.org/10.3390/metabo12030246
Dhandapani R, Thangavelu S, Ragunathan L, Paramasivam R, Velmurugan P, Muthupandian S (2022) Potential bioactive compounds from marine Streptomyces sp. and their in vitro antibiofilm and antibacterial activities against antimicrobial-resistant clinical pathogens. Appl Biochem Biotechnol 194:4702–4723. https://doi.org/10.1007/s12010-022-04072-7
dos Santos RN, Ferreira LG, Andricopulo AD (2018) Practices in molecular docking and structure-based virtual screening. In: Gore M, Jagtap UB (eds) Computational drug discovery and design, methods in molecular biology. Springer Nature, Berlin, pp 31–50. https://doi.org/10.1007/978-1-4939-7756-7_3
Dotolo S, Marabotti A, Facchiano A, Tagliaferri R (2021) A review on drug repurposing applicable to COVID-19. Brief Bioinform 22(2):726–741. https://doi.org/10.1093/bib/bbaa288
Douguet D (2010) e-LEA3D: a computational-aided drug design web server. Nucleic Acid Res 38:W615–W621. https://doi.org/10.1093/nar/gkq322
Dua T, Mangal S, Akshita G, Harshdeep AAK, Sharma P et al (2023) Novel vanillin-based hybrids inhibit quorum sensing and silences phenotypical expressions in Pseudomonas aeruginosa. Drug Dev Res 84:45–61. https://doi.org/10.1002/ddr.22011
Dubey K, Dubey R (2020) Computational screening of narcissoside a glycosyloxyflavone for potential novel coronavirus 2019 (COVID-19) inhibitor. Biomed J 43:363–367. https://doi.org/10.1016/j.bj.2020.05.002
Dubey KK, Indu, Sharma M (2020) Reprogramming of antibiotics to combat antimicrobial resistance. Arch Pharm 2020:e2000168. https://doi.org/10.1002/ardp.202000168
Duenas ME, Peltier-Heap RE, Leveridge M, Annan RS, Buttner FH, Trost M (2023) Advances in high-throughput mass spectrometry in drug discovery. EMBO Mol Med 15:e14850. https://doi.org/10.15252/emmm.202114850
Dugger SA, Platt A, Goldstein DB (2018) Drug development in the era of precision medicine. Nat Rev Drug Discov 17:183–196. https://doi.org/10.1038/nrd.2017.226
Dulsat J, Lopez-Nieto B, Estrada-Tejedor R, Borrell JI (2023) Evaluation of free online ADMET tools for academic or small biotech environments. Molecules 28(2):776. https://doi.org/10.3390/molecules28020776
El Haddad L, Harb CP, Gebara MA, Stibich MA, Chemaly RF (2019) A systematic and critical review of bacteriophage therapy against multidrug-resistant ESKAPE organisms in humans. Clin Infect Dis 69(1):167–178. https://doi.org/10.1093/cid/ciy947
Ellafi A, Farhat R, Snoussi M, Noumi E, Anouar EH, El May MV et al (2023) Phytochemical profiling, antimicrobial, antibiofilm, insecticidal, and anti-leishmanial properties of aqueous extract from Juglans regia L. root bark: in vitro and in silico approaches. Int J Food Prop 26(1):1079–1097. https://doi.org/10.1080/10942912.2023.2200561
Eswaramurthy R, Hailekiros H, Kedir F, Endale M (2021) In silico molecular docking, DFT analysis and ADMET studies of carbazole alkaloid and coumarins from roots of Clausena anisata: a potent inhibitor for quorum sensing. Adv Appl Bioinform Chem 14:13–24. https://doi.org/10.2147/AABC.5290912
Fernandes S, Borges A, Gomes IB, Sousa SF, Simoes M (2023) Curcumin and 10-undecenoic acid as natural quorum sensing inhibitors of LuxS/AI-2 of Bacillus subtilis and LasI/LasR of Pseudomonas aeruginosa. Food Res Int 165:112519. https://doi.org/10.1016/j.foodres.2023.112519
Foscato M, Venkatraman V, Jensen VR (2019) DENOPTIM: software for computational de novo design of organic and inorganic molecules. J Chem Inf Model 59(10):4077–4082. https://doi.org/10.1021/acs.jcim.9b00516
Gajdacs M, Spengler G (2019) The role of drug repurposing in the development of novel antimicrobial drugs: non-antibiotic pharmacological agents as quorum sensing-inhibitors. Antibiotics 8:270. https://doi.org/10.3390/antibiotics8040270
Ganesan A, Coote ML, Barakat K (2017) Molecular dynamics-driven drug discovery: lea** forward with confidence. Drug Discov Today 22(2):249–269. https://doi.org/10.1016/j.drudis.2016.11.001
Gao F, Zhai G, Wang H, Lu L, Xu J, Zhu J, Chen D, Lu H (2020) Protective effects of anti-alginate monoclonal antibody against Pseudomonas aeruginosa infection of HeLa cells. Microb Pathog 145:104240. https://doi.org/10.1016/j.micpath.2020.104240
Ghannay S, Aouadi K, Kadri A, Snoussi M (2022) GC-MS profiling, vibriocidal, antioxidant, antibiofilm, and anti-quorum sensing properties of Carum carvi L. essential oil: in vitro and in silico approaches. Plants 11:1072. https://doi.org/10.3390/plants11081072
Gi M, Jeong J, Lee K, Lee KM, Toyofuku M, Yong DE, Yoon SS, Choi JY (2014) A drug-repositioning screening identifies pentetic acid as a potential therapeutic agent for suppressing the elastase-mediated virulence of Pseudomonas aeruginosa. Antimicrob Agent Chemother 58(12):7205–7214. https://doi.org/10.1128/AAC.03063-14
Giordano D, Biancaniello C, Argenio MA, Facchiano A (2022) Drug design by pharmacophore and virtual screening approach. Pharmaceuticals 15(5):646. https://doi.org/10.3390/ph15050646
Girija ASS, Gnanendra S, Paramasivam A, Vijayashree Priyadharsini J (2021) Delineating the potential targets of thymoquinone in ESKAPE pathogens using a computational approach. In Silico Pharmacol 9:52. https://doi.org/10.1007/s40203-021-00111-z
Gökalsın B, Aksoydan B, Erman B, Sesal NC (2017) Reducing virulence and biofilm of Pseudomonas aeruginosa by potential quorum sensing inhibitor carotenoid: zeaxanthin. Microb Ecol 74:466–473. https://doi.org/10.1007/s00248-017-0949-3
Gulia K, Hassan AHE, Lenhard JR, Farahat AA (2023) Esca** ESKAPE resistance: in vitro and in silico studies of multifunctional carbamimidoyl-tethered indoles against antibiotic-resistant bacteria. R Soc Open Sci 10:230020. https://doi.org/10.1098/rsos230020
Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P (2021) Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 25:1315–1360. https://doi.org/10.1007/s11030-021-10217-3
Hansen T, Alst T, Havelkova M, Strom MB (2010) Antimicrobial activity of small β-peptidomimetics based on the pharmacophore model of short cationic antimicrobial peptides. J Med Chem 53:595–606. https://doi.org/10.1021/jm901052r
Hema Sree GNS, Saraswathy GR, Murahari M, Krishnamurthy M (2019) An update on drug repurposing: re-written saga of the drug’s fate. Biomed Pharmacother 110:700–716. https://doi.org/10.1016/j.biopha.2018.11.127
Hnamte S, Parasuraman P, Ranganathan S, Ampasala DR, Reddy D, Kumavath RN et al (2019) Mosloflavone attenuates the quorum sensing controlled virulence phenotypes and biofilm formation in Pseudomonas aeruginosa PAO1: in vitro, in vivo and in silico approach. Microb Pathog 131:128–134. https://doi.org/10.1016/j.micpath.2019.04.005
Ikhimiukor OO, Odih EE, Donado-Godoy P, Okeke IN (2022) A bottom-up view of antimicrobial resistance transmission in develo** countries. Nat Microbiol 7:757–765. https://doi.org/10.1038/s41564-022-01124-x
Ismail MM, Hassan M, Moawad SS, Okba MM, Ashour RM, Fayek NM, Saber FR (2021) Exploring the antivirulence activity of Pulverulentone A, a phloroglucinol-derivative from Callistemon citrinus leaf extract, against multi-drug resistant Pseudomonas aeruginosa. Antibiotics 10:907. https://doi.org/10.3390/antibiotics10080907
Jarada TN, Rokne JG, Alhajj R (2020) A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions. J Cheminform 12:46. https://doi.org/10.1186/s13321-020-00450-7
Kalpana S, Lin WY, Wang YC, Fu Y, Lakshmi A, Wang HY (2023) Antibiotic resistance diagnosis in ESKAPE pathogens—a review on proteomic perspective. Diagnostics 13(6):1014. https://doi.org/10.3390/diagnostics13061014
Kamarudheen N, Rao KVB (2019) Fatty acyl compounds from marine Streptomyces griseoincarnatus strain HK12 against two major bio-film forming nosocomial pathogens; an in vitro and in silico approach. Microb Pathog 127:121–130. https://doi.org/10.1016/j.micpath.2018.11.050
Kaserer T, Beck KR, Akram M, Odermatt A, Schuster D (2015) Pharmacophore models and pharmacophore-based virtual screening: concepts and applications exemplified on hydroxysteroid dehydrogenases. Molecules 20(12):22799–22832. https://doi.org/10.3390/molecules.201219880
Krell T, Matilla MA (2022) Antimicrobial resistance: progress and challenges in antibiotic discovery and anti-infective therapy. Microb Biotechnol 15(1):70–78. https://doi.org/10.1111/1751-7915.13945
Kumar V, Parate S, Danishuddin, Zeb A, Singh P, Lee G et al (2022) 3D-QSAR-based pharmacophore modeling, virtual screening, and molecular dynamics simulations for the identification of spleen tyrosine kinase inhibitors. Front Cell Infect Microbiol 12:909111. https://doi.org/10.3389/fcimb.2022.909111
Kwon S, Bae H, Jo J, Yoon S (2019) Comprehensive ensemble in QSAR prediction for drug discovery. BMC Bioinform 20:521. https://doi.org/10.1186/s12859-019-3135-4
Larsson DGJ, Flach CF (2022) Antibiotic resistance in the environment. Nat Rev Microbiol 20:257–269. https://doi.org/10.1038/s41579-021-00649-x
Law GL, Tisoncik-Go J, Korth MJ, Katze MG (2013) Drug repurposing: a better approach for infectious disease drug discovery? Curr Opin Microbiol 25:588–592. https://doi.org/10.1016/j.coi.2013.08.004
Lazar V, Holban AM, Curutiu C, Chifiriuc MC (2021) Modulation of quorum sensing and biofilms in less investigated Gram-negative ESKAPE pathogens. Front Microbiol 12:676510. https://doi.org/10.3389/fmicb.2021.676510
Lin TT, Yang LY, Lin CY, Wang CT, Lai CW, Ko CF, Shih YH, Chen SH (2023) Intelligent de novo design of novel antimicrobial peptides against antibiotic-resistant bacteria strains. Int J Mol Sci 24:6788. https://doi.org/10.3390/ijms.24076788
Liu Z, Zhang L, Wang J, Li Y, Chang Y, Huang X et al (2021) Virtual screening and biological evaluation of anti-biofilm agents targeting LuxS in the quorum sensing system. Nat Prod Commun 16(6):1–10. https://doi.org/10.1177/1934578X211019625
Liu G, Thomsen LE, Olsen LE (2022) Antimicrobial-induced horizontal transfer of antimicrobial resistant genes in bacteria: a mini-review. J Antimicrob Ther 77:556–567. https://doi.org/10.1093/jac/dkab450
Lucas JE, Kortemme T (2020) New computational protein design methods for de novo small molecule binding sites. PLoS Comput Biol 16(10):e1008178. https://doi.org/10.1371/journal.pcbi.1008178
Ma Y, Wang YR, He YH, Ding YY, An JX, Zhang ZJ, Zhao WB, Hu YM, Liu YQ (2023) Drug repurposing strategy part 1: from approved drugs to agri-bactericides leads. J Antibiot 76:27–51. https://doi.org/10.1038/s41429-022-00574-y
Maia EHB, Assis LC, de Oliveira TA, da Silva AM, Taranto AG (2020) Structure-based virtual screening: from classical to artificial intelligence. Front Chem 8:343. https://doi.org/10.3389/fchem.2020.00343
Mak KK, Pichika MR (2019) Artificial intelligence in drug development: present status and future prospects. Drug Discov Today 24(3):773–780. https://doi.org/10.1016/j.drudis.2018.11.014
Malandraki-Miller S, Riley PR (2021) Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discov Today 26(4):887–901. https://doi.org/10.1016/j.drudis.2021.01.013
Mansuri A, Lokhande K, Kore S, Gaikwad S, Nawani N, Swamy KV et al (2022) Antioxidant, anti-quorum sensing, biofilm inhibitory activities and chemical composition of Patchouli essential oil: in vitro and in silico approach. J Biomol Struct Dyn 40(1):154–165. https://doi.org/10.1080/07391102.2020.1810124
Matamoros-Recio A, Franco-Gonzalez JF, Forgione RE, Torres-Mozas A, Silipo A, Martin-Santamaria S (2021) Understanding the antibacterial resistance: computational explorations in bacterial membranes. ACS Omega 6:6041–6054. https://doi.org/10.1021/acsomega.0c05590
Meenambiga SS, Rajagopal K (2018) Antibiofilm activity and molecular docking studies of bioactive secondary metabolites from endophytic fungus Aspergillus nidulans on oral Candida albicans. J Appl Pharm Sci 8(3):37–45. https://doi.org/10.7324/JAPS.2018.8306
Melo MCR, Maasch JRMA, de la Fuente-Nunez C (2021) Accelarating antibiotic discovery through artificial intelligence. Commun Biol 4:1050. https://doi.org/10.1038/s42003-021-02586-0
Mishra R, Panda AK, Mandal SD, Shakeel M, Bisht SS, Khan J (2020) Natural anti-biofilm agents: strategies to control biofilm-forming pathogens. Front Microbiol 11:566325. https://doi.org/10.3389/fmicb.2020.566325
Mohamad F, Alzahrani RR, Alsaadi A, Alrfaei BM, Yassin AAB, Alkhulafi MM, Halwani M (2023) An explorative review on advanced approaches to overcome bacterial resistance by curbing bacterial biofilm formation. Infect Drug Resist 16:19–49. https://doi.org/10.2147/IDR.5380883
Mohs RC, Greig NH (2017) Drug discovery and development: role of basic biological research. Alzheimers Dement (N Y) 3(4):651–657. https://doi.org/10.1016/j.trci.2017.10.005
Moingeon P, Kuenemann M, Guedj M (2022) Artificial intelligence-enhanced drug design and development: toward a computational precision medicine. Drug Discov Today 27(1):215–222. https://doi.org/10.1016/j.drudis.2021.09.006
Montiro-Neto V, de Souza CD, Gonzaga LF, da Silveira BC, Sousa NCF, Pontes JP et al (2020) Cuminaldehyde potentiates the antimicrobial actions of ciprofloxacin against Staphylococcus aureus and Escherichia coli. PLoS One 15(5):e0232987. https://doi.org/10.1371/journal.pone.0239287
Mouchlis VD, Afantitis A, Serra A, Fratello M, Papadiamantis AG, Aidinis V et al (2021) Advances in de novo drug design: from conventional to machine learning methods. Int J Mol Sci 22(4):1676. https://doi.org/10.3390/ijms.22041676
Mukherjee A, Bose S, Shaoo A, Das SK (2023) Nanotechnology based therapeutic approaches: an advanced strategy to target the biofilm of ESKAPE pathogens. Mater Adv 4:2544–2572. https://doi.org/10.1039/D2MA00846G
Mulani MS, Kamble EE, Kumkar SN, Tawre MS, Pardesi KR (2019) Emerging strategies to combat ESKAPE pathogens in the era of antimicrobial resistance: a review. Front Microbiol 10:539. https://doi.org/10.3389/fmicb.2019.00539
Nakonieczna J, Wozniak A, Pieranski M, Rapacka-Zdonczyk A, Ogonowska P, Grinholc M (2019) Photoinactivation of ESKAPE pathogens: overview of novel therapeutic strategy. Future Med Chem 11(5):443–461. https://doi.org/10.4155/fmc-2018-0329
Namasivayam SKR, Shankar KG, Vivek JM, Nizar M, Sudarsan AV (2019) In silico and in vitro analysis of quorum quenching active phytochemicals from the ethanolic extract of medicinal plants against quorum sensing mediated virulence factors of Acinetobacter baumannii. Ind J Biochem Biophys 56:276–286
Neves BJ, Braga RC, Melo-Filho CC, Moreira-Filho JT, Muratov EN, Andrade CH (2018) QSAR-based virtual screening: advances and applications in drug discovery. Front Pharmacol 9:1275. https://doi.org/10.3389/fphar.2018.01275
Nicolas-Barreales G, Sujar A, Sanchez A (2021) A web-based tool for simulating molecular dynamics in cloud environments. Electronics 10(2):185. https://doi.org/10.3390/electronics10020185
Noumi E, Ahmad I, Adnan M, Merghni A, Patel H, Haddaji N et al (2023) GC/MS profiling, antibacterial, anti-quorum sensing, and antibiofilm properties of Anethum graveolens L. essential oil: molecular docking study and in-silico ADME profiling. Plants 12:1997. https://doi.org/10.3390/plants12101997
Nyawai TN, Asaruddin MR, Rosli MFA, Romli AM, Radhakrishnan SE, Ahmad MN (2017) Pharmacophore modeling of phytochemicals from Clinacanthus nutans for antimicrobial activity. Transact Sci Technol 4(4):498–503
Opo FADM, Rahman MM, Ahammad F, Ahmed I, Bhuiyan MA, Asiri AM (2021) Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein. Sci Rep 11:4049. https://doi.org/10.1038/s41598-021-83626-x
Ou-yang S, Lu J, Kong X, Liang Z, Luo C, Jiang H (2012) Computational drug discovery. Acta Pharmacol Sin 33:1131–1140. https://doi.org/10.1038/aps.2012.109
Pagadala NS, Syed K, Tuszynski J (2017) Software for molecular docking: a review. Biophys Rev 9:91–102. https://doi.org/10.1007/s12551-016-0247-1
Palmer N, Massch JRMA, Torres MDT, de la Fuente-Nunez C (2021) Molecular dynamics for antimicrobial peptide discovery. Infect Immun 89:e00703-20. https://doi.org/10.1128/IAI.00703-20
Panda SK, Buroni S, Swain SS, Bonacorsi A, da Fonseca Amorim EA, Kulshreshtha M, da Silva LCN, Tiwari V (2022) Recent advances to combat ESKAPE pathogens with special reference to essential oils. Front Microbiol 13:1029098. https://doi.org/10.3389/fmicb.2022.1029098
Pant N, Miranda-Hernandez S, Rush C, Warner J, Eisen DP (2022) Non-antimicrobial adjuvant therapy using Ticagrelor reduced biofilm-related Staphylococcus aureus prosthetic joint infection. Front Pharamacol 13:927783. https://doi.org/10.3389/fphar.2022.927783
Park K (2019) A review of computational drug repurposing. Transl Clin Pharmacol 27(2):59–63. https://doi.org/10.12793/tcp.2019.27.2.59
Patel A, Banerji R, Kanojiya P, Saroj SD (2021) Foodborne ESKAPE biofilms and antimicrobial resistance: lessons learned from clinical isolates. Pathog Glob Health 115(6):339–356. https://doi.org/10.1010/20477724.2021.1916158
Pattnaik SS, Ranganathan S, Ampasala DR, Syed A, Ameen F, Busi S (2018) Attenuation of quorum sensing regulated virulence and biofilm development in Pseudomonas aeruginosa PAO1 by Diaporthe phaseolorum SSP12. Microb Pathog 118:177–189. https://doi.org/10.1016/j.micpath.2018.03.031
Paudel A, Kaneko K, Watanabe A, Shigeki M, Motomu K, Hamamoto H, Sekimizu K (2013) Structure-activity relationship study of novel iminothiadiazolo-pyrimidinone antimicrobial agents. J Antibiot 66:663–667. https://doi.org/10.1038/ja.2013.69
Pfalzgraff A, Brandenburg K, Weindl G (2018) Antimicrobial peptides and their therapeutic potential for bacterial skin infections and wounds. Front Pharmacol 9:281. https://doi.org/10.3389/fphar.2018.00281
Prachayasittikul V, Worachartcheewan A, Shumbuatong W, Songtawee N, Simeon S, Prachayasittikul V, Nantasenamat C (2015) Computer-aided drug design of bioactive natural products. Curr Top Med Chem 15:1780–1800. https://doi.org/10.2174/1568026615666150506151101
Priyamvada P, Debroy R, Anbarasu A, Ramaiah S (2022) A comprehensive review on genomics, systems biology and structural biology approaches for combating antimicrobial resistance in ESKAPE pathogens: computational tools and recent advancements. World J Microbiol Biotechnol 38:153. https://doi.org/10.1007/s11274-022-03343-z
Qin HL, Zhang ZW, Ravindar L, Rakesh KP (2020) Antibacterial activities with the structure-activity relationship of coumarin derivatives. Eur J Med Chem 207:112832. https://doi.org/10.1016/j.ejmech.2020.112832
Qureshi KA, Imtiaz M, Parvez A, Rai PK, Jaremko M, Emwas AH et al (2022) In vitro and in silico approaches for the evaluation of antimicrobial activity, time-kill kinetics, and anti-biofilm potential of thymoquinone (2-methyl-5-propan-2-ylcyclohexa-2,5-diene-1,4-dione) against selected human pathogens. Antibiotics 11(1):79. https://doi.org/10.3390/antibiotic.11010079
Rabha J, Chetri BK, Das S, Jha DK (2023) In-vitro and in-silico evaluation of antimicrobial and antibiofilm secondary metabolites of a novel fungal endophyte, Albophoma sp. BAPR5. South Afr J Bot 158:347–368. https://doi.org/10.1016/j.sajb.2023.05.033
Rajput A, Kumar M (2018) Anti-biofilm peptides: a new class of quorum quenchers and their prospective therapeutic applications. In: Kalia VC (ed) Biotechnological applications of quorum sensing inhibitors. Springer Nature, Berlin, pp 87–110. https://doi.org/10.1007/978-981-10-9026-4_5
Rajput A, Bhamare KT, Thakur A, Kumar M (2023) Anti-biofilm: machine learning assisted prediction of IC50 activity of chemicals against biofilms of microbes causing antimicrobial resistance and implications in drug repurposing. J Mol Biol 435:168115. https://doi.org/10.1016/j.jmb.2023.168115
Rasheed MA, Iqbal MN, Saddick S, Ali I, Khan FS, Kanwal S et al (2021) Identification of lead compounds against Scm (fms10) in Enterococcus faecium using computer-aided drug designing. Life 11:77. https://doi.org/10.3390/life11020077
Sadiq S, Rana NF, Zahid MA, Zargaham MK, Tanweer T, Batool A et al (2020) Virtual screening of FDA-approved drugs against LasR of Pseudomonas aeruginosa for antibiofilm potential. Molecules 25:3723. https://doi.org/10.3390/molecules25163723
Sadybekov AV, Katritch V (2023) Computational approaches streamlining drug discovery. Nature 616:673–685. https://doi.org/10.1038/s41586-023-05905-z
Sagulkoo P, Chuntakaruk H, Rungrotmongkol T, Suratanee A, Plaimas K (2022) Multi-level biological network analysis and drug repurposing based on leukocyte transcriptomics in severe COVID-19: in silico systems biology to precision medicine. J Pers Med 12(7):1030. https://doi.org/10.3390/jpm12071030
Samreen, Qais FA, Ahmad I (2022) Anti-quorum sensing and biofilm inhibitory effect of some medicinal plants against Gram-negative bacterial pathogens: in vitro and in silico investigations. Heliyon 8:e11113. https://doi.org/10.1016/j.heliyon.2022.e11113
Schaller D, Sribar D, Noonan T, Deng L, Nguyen TN, Pach S et al (2020) Next generation 3D pharmacophore modeling. WIREs Comput Mol Sci 10(4):e1468. https://doi.org/10.1002/wcms.1468
Schneider G, Fechner U (2005) Computer-based de novo design of drug-like molecules. Nat Rev Drug Dis 4:649–663. https://doi.org/10.1038/nrd1799
Schottlender G, Prieto JM, Palumbo MC, Castello FA, Serral F, Sossa EJ et al (2022) From drugs to targets: reverse engineering the virtual screening process on a proteomic scale. Front Drug Discov 2:969983. https://doi.org/10.3389/fddsv.2022.969983
Schultz F, Anywar G, Tang H, Chassagne F, Lyles JT, Garbe LA, Quave CL (2020) Targeting ESKAPE pathogens with anti-infective medicinal plants from the Greater Mpigi region in Uganda. Sci Rep 10:11935. https://doi.org/10.1038/s41598-020-67572-8
Seethalakshmi PS, Charity OJ, Giakoumis T, Kiran GS, Sriskandan S, Voulvoulis N, Selvin J (2022) Delineating the impact of COVID-19 on antimicrobial resistance: an Indian perspective. Sci Total Environ 818:151702. https://doi.org/10.1016/j.scitotenv.2021.151702
Shamim A, Ali A, Iqbal Z, Mirza MA, Aqil M, Kawish SM et al (2023) Natural medicine a promising candidate in combating microbial biofilm. Antibiotics 12(2):299. https://doi.org/10.3390/antibiotics12020299
Sharma A, Gupta P, Kumar R, Bhardwaj A (2016) dPABBs: a novel in silico approach for predicting and designing anti-biofilm peptides. Sci Rep 6:21839. https://doi.org/10.1038/srep21839
Sharma S, Gopu V, Sivasankar C, Shetty PH (2019) Hydrocinnamic acid produced by Enterobacter xiangfangensis impairs AHL-based quorum sensing and biofilm formation in Pseudomonas aeruginosa. RSC Adv 9:28678. https://doi.org/10.1039/c9ra05725k
Sharma PP, Bansal M, Sethi A, Poonam PL, Goel VK et al (2021a) Computational methods directed towards drug repurposing for COVID-19: advantages and limitations. RSC Adv 11:36181. https://doi.org/10.1039/d1ra05320e
Sharma S, Tiwari M, Tiwari V (2021b) Therapeutic strategies against authophagic escape by pathogenic bacteria. Drug Discov Today 26(3):704–712. https://doi.org/10.1016/j.drudis.2020.12.002
Shen L, Feng H, Qiu Y, Wei GW (2023) SVSBI: sequence-based virtual screening of biomolecular interactions. Commun Biol 6(1):536. https://doi.org/10.1038/s42003-023-04866-3
Shukla A, Shukla G, Parmar P, Patel B, Goswami D, Saraf M (2021) Exemplifying the next generation of antibiotic susceptibility intensifiers of phytochemicals by LasR-mediated quorum sensing inhibition. Sci Rep 11:22421. https://doi.org/10.1038/s41598-021-01845-8
Sicho M, Liu X, Svozil D, van Westen GJP (2021) GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics. J Cheminform 13:73. https://doi.org/10.1186/s13321-021-00550-y
Sobieszczanska N, Myszka K, Szwengiel A, Majcher M, Grygier A, Wolko L (2020) Tarragon essential oil as a source of bioactive compounds with anti-quorum sensing and anti-proteolytic activity against Pseudomonas spp. isolated from fish—in vitro, in silico and in situ approaches. Int J Food Microbiol 331:108732. https://doi.org/10.1016/j.ijfoodmicro.2020.108732
Srivastava GN, Malwe AS, Sharma AK, Shastri V, Hibare K, Sharma VK (2020) Molib: a machine learning based classification tool for the prediction of biofilm inhibitory molecules. Genomics 112:2823–2832. https://doi.org/10.1016/j.ygeno.2020.03.020
Stephens LJ, Werrett MV, Sedgwick AC, Bull SD, Andrews PC (2020) Antimicrobial innovation: a current update and perspective on the antibiotic drug development pipeline. Future Med Chem 12(22):2035–2065. https://doi.org/10.4155/fmc-2020-0225
Suay-Garcia B, Bueso-Bordils JI, Falco A, Perez-Garcia MT, Anton-Fos G, Aleman-Lopez P (2020a) Quantitative structure-activity relationship methods in the discovery and development of antibacterials. WIREs Comput Mol Sci 10(6):e1472. https://doi.org/10.1002/wcms.1472
Suay-Garcia B, Falco A, Bueso-Bordlis JI, Anton-Fos GM, Perez-Garcia MT, Aleman-Lopez PA (2020b) Tree-based QSAR model for drug repurposing in the discovery of new antibacterial compounds against Escherichia coli. Pharmaceuticals 13(12):431. https://doi.org/10.3390/ph13120431
Sun S, Dai X, Sun J, Bu X, Weng C, Li H, Zhu H (2016) A diketopiperazine factor from Rheinheimera aquimaris QSI02 exhibits anti-quorum sensing activity. Sci Rep 6:39637. https://doi.org/10.1038/srep39637
Swamidass SJ (2011) Mining small-molecule screens to repurpose drugs. Brief Bioinform 12(4):327–335. https://doi.org/10.1093/bib/bbr028
Talat A, Khan AU (2023) Artificial intelligence as a smart approach to develop antimicrobial drug molecules: a paradigm to combat drug-resistant infections. Drug Discov Today 28(4):103491. https://doi.org/10.1016/j.drudis.2023.103491
Tang Y, Zou F, Chen C, Zhang Y, Shen Z, Liu Y et al (2023) Antibacterial and antibiofilm activities of Sertindole and its antibacterial mechanism against Staphylococcus aureus. ACS Omega 8:5415–5425. https://doi.org/10.1021/acsomega.2c06569
Tapia-Rodriguez MR, Bernal-Mercado AT, Gutierrez-Pacheco MM, Vazquez-Armenta FJ, Hernandez-Mendoza A, Gonzalez-Aguilar GA et al (2019) Virulence of Pseudomonas aeruginosa exposed to carvacrol: alterations of the quorum sensing at enzymatic and gene levels. J Cell Commun Signaling 13:531–537. https://doi.org/10.1007/s12079-019-00516-8
Torres PHM, Sodero ACR, Jofily P, Silva-Jr FP (2019) Key topics in molecular docking for drug design. Int J Mol Sci 20(18):4574. https://doi.org/10.3390/ijms20184574
Tripathi MK, Nath A, Singh TP, Ethayathulla AS, Kaur P (2021) Evolving scenario of big data and artificial intelligence (AI) in drug discovery. Mol Divers 25:1439–1460. https://doi.org/10.1007/s11030-021-10256-w
Truong TTT, Panizzutti B, Kim JH, Walder K (2022) Repurposing drugs via network analysis: opportunities for psychiatric disorders. Pharmaceutics 14(7):1464. https://doi.org/10.3390/pharmaceutics14071464
Tyagi R, Singh A, Chaudhary KK, Yadav MK (2022) Pharmacophore modeling and its applications. In: Singh DB, Pathak RK (eds) Bioinformatics: methods and applications. Academic, New York, pp 269–289. https://doi.org/10.1016/B978-0-323-89775-4.00009-2
Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M, Zhao S (2019) Applications of machine learning in drug discovery and development. Nat Rev Drug Dis 18:463–477. https://doi.org/10.1038/s41573-019-0024-5
Vestby LK, Grønseth T, Simm R, Nesse LL (2020) Bacterial biofilms and its role in the pathogenesis of disease. Antibiotics 9(2):59. https://doi.org/10.3390/antibiotics9020059
Vieira TF, Magalhaes RP, Simoes M, Sousa SF (2022) Drug repurposing targeting Pseudomonas aeruginosa MvfR using docking, virtual screening, molecular dynamics, and free-energy calculations. Antibiotics 11:185. https://doi.org/10.3390/antibiotics11020185
Vrancianu CO, Gheorghe I, Dobre EG, Barbu IC, Cristian RE, Popa M et al (2020) Emerging strategies to combat β-lactamase producing ESKAPE pathogens. Int J Mol Sci 21(22):8527. https://doi.org/10.3390/ijms21228527
Wang L, Ding J, Pan L, Cao D, Jiang H, Ding X (2019) Artificial intelligence facilitates drug design in the big data era. Chemometr Intell Lab Syst 194:103850. https://doi.org/10.1016/j.chemolab.2019.103850
Wang C, **ong Y, Bao C, Wei Y, Wen Z, Cao X et al (2023) Antibacterial and anti-biofilm activity of radezolid against Staphylococcus aureus clinical isolates from China. Front Microbiol 14:1131178. https://doi.org/10.3389/fmicb.2023.1131178
Wu B, Yang X, Yan M (2019) Synthesis and structure-activity relationship study of antimicrobial Auranofin against ESKAPE pathogens. J Med Chem 62(17):7751–7768. https://doi.org/10.1021/acs.jmedchem.9b00550
Wu J, ** K, Jiao K, Wang X, Li S, Pan L (2023) CarbonAI, a non-docking deep learning based small molecule virtual screening platform. ChemRxiv. https://doi.org/10.26434/chemrxiv-2022-gk3n6-v2
**ong G, Wu Z, Yi J, Fu L, Yang Z, Hsieh C et al (2021) ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acid Res 49:W5–W14. https://doi.org/10.1093/nar/gkab255
Yang F, Zhang Q, Ji X, Zhang Y, Li W, Peng S, Xue F (2022) Machine learning applications in drug repurposing. Interdiscip Sci 14(1):15–21. https://doi.org/10.1007/s12539-021-00487-8
Ye J, Yang X, Ma C (2023) Ligand-based drug design of novel antimicrobials against Staphylococcus aureus by targeting bacterial transcription. Int J Mol Sci 24(1):339. https://doi.org/10.3390/ijms24010339
Yu W, MacKerell AD Jr (2017) Computer-aided drug design methods. Methods Mol Biol 1520:85–106. https://doi.org/10.1007/978-1-4939-6634-9_5
Zeng YX, Liu JS, Wang YJ, Tang S, Wang DY, Deng SM, Jia AQ (2022) Actinomycin D: a novel Pseudomonas aeruginosa quorum sensing inhibitor from the endophyte Streptomyces cyaneochromogenes RC1. World J Microbiol Biotechnol 38:170. https://doi.org/10.1007/s11274-022-03360-y
Zhang M, Prior AM, Maddox MM, Shen WJ, Hevener KE, Bruhn DF et al (2018) Pharmacophore modeling, synthesis, and antibacterial evaluation of chalcones and derivatives. ACS Omega 3:18343–18360. https://doi.org/10.1021/acsomega.8b03174
Zhang Y, Zhang Y, Chen C, Cheng H, Deng X, Li D et al (2022) Antibacterial activities and action mode of anti-hyperlipidemic lomitapide against Staphylococcus aureus. BMC Microbiol 22:114. https://doi.org/10.1186/s12866-022-02535-9
Zhou JW, Jia AQ, Jiang H, Li PL, Chen H, Tan XJ, Liu EQ (2021) 1-(4-Amino-2-hydroxyphenyl)ethanone from Phomopsis liquidambari showed quorum sensing inhibitory activity against Pseudomonas aeruginosa. Appl Microbiol Biotechnol 105(1):341–352. https://doi.org/10.1007/s00253-020-11013-z
Zong N, Wen A, Moon S, Fu S, Wang L, Zhao Y et al (2022) Computational drug repurposing based on electronic health records: a sco** review. Npj Digital Med 5:77. https://doi.org/10.1038/s41746-022-00617-6
Acknowledgement
The author, Subhaswaraj Pattnaik, acknowledges SERB DST, Govt. of India for the National Post-Doctoral Fellowship (NPDF) (Reference No. PDF/2021/001260). The authors would also like to acknowledge OHEPEE, Govt. of Odisha, through the World Bank for giving financial support.
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Pattnaik, S., Mishra, M., Naik, P.K. (2024). Computational Approaches for the Inhibition of ESKAPE Pathogens. In: Busi, S., Prasad, R. (eds) ESKAPE Pathogens. Springer, Singapore. https://doi.org/10.1007/978-981-99-8799-3_19
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