A data-driven machine learning approach for discovering potent LasR inhibitors

References

• Langendonk RF, Neill DR, Fothergill JL. The building blocks of antimicrobial resistance in Pseudomonas aeruginosa: Implications for current resistance-breaking therapies. Front Cell Infect Microbiol. 2021;11:665759. doi: 10.3389/fcimb.2021.665759
   PubMed Web of Science ®Google Scholar
• Hsu J. How COVID-19 is accelerating the threat of antimicrobial resistance. BMJ. 2020;369:m1983. doi: 10.1136/bmj.m1983
   PubMed Web of Science ®Google Scholar
• Strateva T, Yordanov D. Pseudomonas aeruginosa – a phenomenon of bacterial resistance. J Med Microbiol. 2009;58(9):1133–342. doi: 10.1099/jmm.0.009142-0
   PubMed Web of Science ®Google Scholar
• Hancock RE. Resistance mechanisms in Pseudomonas aeruginosa and other nonfermentative gram-negative bacteria. Clin Infect Dis. 1998;27(Suppl 1):S93–99. doi: 10.1086/514909
   PubMed Web of Science ®Google Scholar
• Lambert PA. Mechanisms of antibiotic resistance in Pseudomonas aeruginosa. J R Soc Med. 2002;95(Suppl 41):22–26.
   PubMed Web of Science ®Google Scholar
• Ding F, Oinuma K-I, Smalley NE, et al. The Pseudomonas aeruginosa orphan quorum sensing signal receptor QscR regulates global quorum sensing gene expression by activating a single linked operon. MBio. 2018;9(4): doi: 10.1128/mBio.01274-18
   Web of Science ®Google Scholar
• Rasmussen TB, Givskov M. Quorum sensing inhibitors: a bargain of effects. Microbiol (Reading). 2006;152(4):895–904. doi: 10.1099/mic.0.28601-0
   PubMedGoogle Scholar
• Pearson JP, Gray KM, Passador L, et al. Structure of the autoinducer required for expression of Pseudomonas aeruginosa virulence genes. Proc Nat Acad Sci. 1994;91(1):197–201. doi: 10.1073/pnas.91.1.197
   PubMed Web of Science ®Google Scholar
• Gambello MJ, Kaye S, Iglewski BH. LasR of Pseudomonas aeruginosa is a transcriptional activator of the alkaline protease gene (apr) and an enhancer of exotoxin a expression. Infect Immun. 1993;61(4):1180–1184. doi: 10.1128/iai.61.4.1180-1184.1993
   PubMed Web of Science ®Google Scholar
• Elnegery AA, Mowafy WK, Zahra TA, et al. Study of quorum-sensing LasR and RhlR genes and their dependent virulence factors in Pseudomonas aeruginosa isolates from infected burn wounds. Access Microbiol. 2021;3(3):000211. doi: 10.1099/acmi.0.000211
   PubMedGoogle Scholar
• Wang Y, Gao L, Rao X, et al. Characterization of lasR-deficient clinical isolates of Pseudomonas aeruginosa. Sci Rep. 2018;8(1):13344. doi: 10.1038/s41598-018-30813-y
   PubMedGoogle Scholar
• Cocotl-Yañez M, Soto-Aceves MP, González-Valdez A, et al. Virulence factors regulation by the quorum-sensing and Rsm systems in the marine strain Pseudomonas aeruginosa ID4365, a natural mutant in lasR. FEMS Microbiol Lett. 2020;367(12). doi: 10.1093/femsle/fnaa092
   Web of Science ®Google Scholar
• Kalia VC, Purohit HJ. Quenching the quorum sensing system: potential antibacterial drug targets. Crit Rev Microbiol. 2011;37(2):121–140. doi: 10.3109/1040841X.2010.532479
   PubMed Web of Science ®Google Scholar
• Williams P, Cámara M. Quorum sensing and environmental adaptation in Pseudomonas aeruginosa: a tale of regulatory networks and multifunctional signal molecules. Curr Opin Microbiol. 2009;12(2):182–191. doi: 10.1016/j.mib.2009.01.005
   PubMed Web of Science ®Google Scholar
• Kim K, Kim YU, Koh BH, et al. HHQ and PQS, two Pseudomonas aeruginosa quorum‐sensing molecules, down‐regulate the innate immune responses through the nuclear factor‐κB pathway. Immunology. 2010;129(4):578–588. doi: 10.1111/j.1365-2567.2009.03160.x
   PubMed Web of Science ®Google Scholar
• Müsken M, Di Fiore S, Dötsch A, et al. Genetic determinants of Pseudomonas aeruginosa biofilm establishment. Microbiology. 2010;156(2):431–441. doi: 10.1099/mic.0.033290-0
   PubMedGoogle Scholar
• Lesic B, Lépine F, Déziel E, et al. Inhibitors of pathogen intercellular signals as selective anti-infective compounds. PLOS Pathogens. 2007;3(9):e126. doi: 10.1371/journal.ppat.0030126
   PubMed Web of Science ®Google Scholar
• Komor U, Bielecki P, Loessner H, et al. Biofilm formation by Pseudomonas aeruginosa in solid murine tumors–a novel model system. Microbes Infect. 2012;14(11):951–958. doi: 10.1016/j.micinf.2012.04.002
   PubMed Web of Science ®Google Scholar
• Rampioni G, Falcone M, Heeb S, et al. Unravelling the genome-wide contributions of specific 2-Alkyl-4-quinolones and PqsE to quorum sensing in Pseudomonas aeruginosa. PLOS Pathogens. 2016;12(11):e1006029. doi: 10.1371/journal.ppat.1006029
   PubMed Web of Science ®Google Scholar
• O’Brien KT, Noto JG, Nichols-O’Neill L, et al. Potent irreversible inhibitors of LasR quorum sensing in Pseudomonas aeruginosa. ACS Med Chem Lett. 2015;6(2):162–167. doi: 10.1021/ml500459f
   PubMed Web of Science ®Google Scholar
• Rasko DA, Sperandio V. Anti-virulence strategies to combat bacteria-mediated disease. Nat Rev Drug Discov. 2010;9(2):117–128. doi: 10.1038/nrd3013
   PubMed Web of Science ®Google Scholar
• Bottomley MJ, Muraglia E, Bazzo R, et al. Molecular insights into quorum sensing in the human pathogen Pseudomonas aeruginosa from the structure of the virulence regulator LasR bound to its autoinducer. J Biol Chem. 2007;282(18):13592–13600. doi: 10.1074/jbc.M700556200
   PubMed Web of Science ®Google Scholar
• Zou Y, Nair SK. Molecular basis for the recognition of structurally distinct autoinducer mimics by the Pseudomonas aeruginosa LasR quorum-sensing signaling receptor. Chem Biol. 2009;16(9):961–970. doi: 10.1016/j.chembiol.2009.09.001
   PubMedGoogle Scholar
• Kiratisin P, Tucker KD, Passador L. LasR, a transcriptional activator of Pseudomonas aeruginosa virulence genes, functions as a multimer. J Bacteriol. 2002;184(17):4912–4919. doi: 10.1128/jb.184.17.4912-4919.2002
   PubMed Web of Science ®Google Scholar
• Schuster M, Urbanowski ML, Greenberg EP. Promoter specificity in Pseudomonas aeruginosa quorum sensing revealed by DNA binding of purified LasR. Proc Natl Acad Sci U S A. 2004;101(45):15833–15839. doi: 10.1073/pnas.0407229101
   PubMed Web of Science ®Google Scholar
• O’Reilly MC, Dong S-H, Rossi FM, et al. Structural and biochemical studies of non-native agonists of the LasR quorum-sensing receptor reveal an L3 loop “out” conformation for LasR. Cell Chem Biol. 2018;25(9):1128–1139.e1123. doi: 10.1016/j.chembiol.2018.06.007
   PubMed Web of Science ®Google Scholar
• Shah M, Taylor VL, Bona D, et al. A phage-encoded anti-activator inhibits quorum sensing in Pseudomonas aeruginosa. Mol Cell. 2021;81(3):571–583.e576. doi: 10.1016/j.molcel.2020.12.011
   PubMed Web of Science ®Google Scholar
• Paczkowski JE, McCready AR, Cong J-P, et al. An autoinducer analogue reveals an alternative mode of ligand binding for the LasR quorum-sensing receptor. ACS Chem Biol. 2019;14(3):378–389. doi: 10.1021/acschembio.8b00971
   PubMed Web of Science ®Google Scholar
• Gerdt JP, McInnis CE, Schell TL, et al. Mutational analysis of the quorum-sensing receptor LasR reveals interactions that govern activation and inhibition by nonlactone ligands. Chem Biol. 2014;21(10):1361–1369. doi: 10.1016/j.chembiol.2014.08.008
   PubMedGoogle Scholar
• Stacy DM, Le Quement ST, Hansen CL, et al. Synthesis and biological evaluation of triazole-containing N-acyl homoserine lactones as quorum sensing modulators. Org Biomol Chem. 2013;11(6):938–954. doi: 10.1039/C2OB27155A
   PubMed Web of Science ®Google Scholar
• Srinivasarao S, Nandikolla A, Nizalapur S, et al. Design, synthesis and biological evaluation of 1,2,3-triazole based 2-aminobenzimidazoles as novel inhibitors of LasR dependent quorum sensing in Pseudomonas aeruginosa. RSC Adv. 2019;9(50):29273–29292. doi: 10.1039/C9RA05059K
   PubMed Web of Science ®Google Scholar
• Srinivasarao S, Nizalapur S, Yu TT, et al. Design, synthesis and biological evaluation of triazole-containing 2-phenylindole and salicylic acid as quorum sensing inhibitors against Pseudomonas aeruginosa. ChemistrySelect. 2018;3(32):9170–9180. doi: 10.1002/slct.201801622
   Web of Science ®Google Scholar
• Jiang Q, Chen J, Yang C, et al. Quorum sensing: A prospective therapeutic target for bacterial diseases. Biomed Res Int. 2019;2019:2015978. doi: 10.1155/2019/2015978
   PubMed Web of Science ®Google Scholar
• Ozer EA, Pezzulo A, Shih DM, et al. Human and murine paraoxonase 1 are host modulators of Pseudomonas aeruginosa quorum-sensing. FEMS Microbiol Lett. 2005;253(1):29–37. doi: 10.1016/j.femsle.2005.09.023
   PubMed Web of Science ®Google Scholar
• Yang F, Wang L-H, Wang J, et al. Quorum quenching enzyme activity is widely conserved in the sera of mammalian species. FEBS Lett. 2005;579(17):3713–3717. doi: 10.1016/j.febslet.2005.05.060
   PubMed Web of Science ®Google Scholar
• Cady NC, McKean KA, Behnke J, et al. Inhibition of biofilm formation, quorum sensing and infection in Pseudomonas aeruginosa by natural products-inspired organosulfur compounds. Plos One. 2012;7(6):e38492. doi: 10.1371/journal.pone.0038492
   PubMed Web of Science ®Google Scholar
• Bortolotti D, Trapella C, Bragonzi A, et al. Conjugation of LasR quorum-sensing inhibitors with ciprofloxacin decreases the antibiotic tolerance of P. aeruginosa clinical strains. J Chem. 2019;2019:1–13. DOI:10.1155/2019/8143739
   Web of Science ®Google Scholar
• Abbas H, Shaldam M, Eldamasi D. Curtailing quorum sensing in Pseudomonas aeruginosa by sitagliptin. Curr Microbiol. 2020;77(6):1051–1060. doi: 10.1007/s00284-020-01909-4
   PubMed Web of Science ®Google Scholar
• Hançer Aydemir D, Çifci G, Aviyente V, et al. Quorum-sensing inhibitor potential of trans-anethole against Pseudomonas aeruginosa. J Appl Microbiol. 2018;125(3):731–739. doi: 10.1111/jam.13892
   PubMed Web of Science ®Google Scholar
• Aldewachi H, Al-Zidan RN, Conner MT, et al. High-throughput screening platforms in the discovery of novel drugs for neurodegenerative diseases. Bioeng (Basel). 2021;8(2):30. doi: 10.3390/bioengineering8020030
   PubMedGoogle Scholar
• Waring MJ, Arrowsmith J, Leach AR, et al. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat Rev Drug Discov. 2015;14(7):475–486. doi: 10.1038/nrd4609
   PubMed Web of Science ®Google Scholar
• OpenEye Scientific Software. OMEGA 4.1.2. 2021. Available from: https://docs.eyesopen.com/applications/omega/releasenotes/version4_1_2.html
   Google Scholar
• Reymond J-L. The chemical space project. Acc Chem Res. 2015;48(3):722–730. doi: 10.1021/ar500432k
   PubMed Web of Science ®Google Scholar
• Yeung AWK, Atanasov AG, Sheridan H, et al. Open innovation in medical and pharmaceutical research: A literature landscape analysis. Front Pharmacol. 2021;11. doi: 10.3389/fphar.2020.587526
   PubMed Web of Science ®Google Scholar
• Erlanson DA, Fesik SW, Hubbard RE, et al. Twenty years on: the impact of fragments on drug discovery. Nat Rev Drug Discov. 2016;15(9):605–619. doi: 10.1038/nrd.2016.109
   PubMed Web of Science ®Google Scholar
• Doak BC, Norton RS, Scanlon MJ. The ways and means of fragment-based drug design. Pharmacol Ther. 2016;167:28–37. doi:10.1016/j.pharmthera.2016.07.003
   PubMed Web of Science ®Google Scholar
• Rees S. The promise of open innovation in drug discovery: an industry perspective. Future Med Chem. 2015;7(14):1835–1838. doi: 10.4155/fmc.15.125
   PubMed Web of Science ®Google Scholar
• Keserű GM, Erlanson DA, Ferenczy GG, et al. Design principles for fragment libraries: maximizing the value of learnings from pharma Fragment-Based Drug Discovery (FBDD) programs for use inacademia. J Med Chem. 2016;59(18):8189–8206. doi: 10.1021/acs.jmedchem.6b00197
   PubMed Web of Science ®Google Scholar
• Vivona R, Demircioglu MA, Audretsch DB. The costs of collaborative innovation. J Technol Transf. 2022;48(3):873–899. doi: 10.1007/s10961-022-09933-1
   Web of Science ®Google Scholar
• Drăgulescu B, Bucos M. Hyperparameter Tuning Using Automated Methods to Improve Models for Predicting Student Success. In: Lopata A, Butkienė R, Gudonienė D, and Sukackė V, editors. Information and software technologies. Vol. 1283.; Springer Nature Switzerland AG: Springer International Publishing; 2020. p. 309–320.
   Google Scholar
• Wu Z, Zhu M, Kang Y, et al. Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets. Brief Bioinform. 2020;22(4): doi: 10.1093/bib/bbaa321
   Web of Science ®Google Scholar
• Zhao Y, Yin X, Fu Y, et al. A comparative mapping of plant species diversity using ensemble learning algorithms combined with high accuracy surface modelling. Environ Sci Pollut Res. 2022;29(12):17878–17891. doi: 10.1007/s11356-021-16973-x
   PubMed Web of Science ®Google Scholar
• Gerdt JP, Blackwell HE. Competition studies confirm two major barriers that can preclude the spread of resistance to quorum-sensing inhibitors in bacteria. ACS Chem Biol. 2014;9(10):2291–2299. doi: 10.1021/cb5004288
   PubMed Web of Science ®Google Scholar
• Mellbye B, Schuster M. The sociomicrobiology of antivirulence drug resistance: a proof of concept. MBio. 2011;2(5):e00131–00111. doi: 10.1128/mBio.00131-11
   PubMed Web of Science ®Google Scholar
• Allen RC, Popat R, Diggle SP, et al. Targeting virulence: can we make evolution-proof drugs? Nature Rev Microbiol. 2014;12(4):300–308. doi: 10.1038/nrmicro3232
   PubMed Web of Science ®Google Scholar
• Clatworthy AE, Pierson E, Hung DT. Targeting virulence: a new paradigm for antimicrobial therapy. Nat Chem Biol. 2007;3(9):541–548. doi: 10.1038/nchembio.2007.24
   PubMed Web of Science ®Google Scholar
• Hall M, Frank E, Holmes G, et al. The WEKA data mining software. ACM SIGKDD Explor Newsl. 2009;11(1):10–18. doi: 10.1145/1656274.1656278
   Google Scholar
• Chauhan H, Kumar V, Pundir S, et al. Comparative analysis and research Issues in classification techniques for intrusion detection. Intelligent Computing, Networking, and Informatics. Springer. 2014;675–685. doi: 10.1007/978-81-322-1665-0_68
   Google Scholar
• Enamine. Advanced Collection. 2021. Available from: https://enamine.net/compound-collections/screening-collection/advanced-collection
   Google Scholar
• OpenEye Scientific Software. ROCS 3.4.3. 2021. Available from: https://docs.eyesopen.com/applications/rocs/releasenotes/version3_4_3.html
   Google Scholar
• OpenEye Scientific Software. EON 2.3.6. 2021. Available from: https://docs.eyesopen.com/applications/eon/releasenotes/version2_3_6.html
   Google Scholar
• The Brinkman Lab. Pseudomonas aeruginosa PA7, PSPA7_3898 (lasR). 2022. Available from: https://www.pseudomonas.com/feature/show/?id=1670586&view=sequence
   Google Scholar
• The Brinkman Lab. Pseudomonas aeruginosa PAO1, PA1430 (lasR). 2022. Available from: https://www.pseudomonas.com/feature/show/?id=105628&view=sequence
   Google Scholar
• The Brinkman Lab. Pseudomonas aeruginosa UCBPP-PA14, PA14_45960 (lasR). 2022. Available from: https://www.pseudomonas.com/feature/show/?id=1658248&view=sequence
   Google Scholar
• The Brinkman Lab. Pseudomonas aeruginosa PAK, PAKAF_RS18560 (lasR). 2022. Available from: https://www.pseudomonas.com/feature/show/?id=237052075&view=sequence
   Google Scholar
• Winsor GL, Griffiths EJ, Lo R, et al. Enhanced annotations and features for comparing thousands of Pseudomonas genomes in the Pseudomonas genome database. Nucleic Acids Res. 2016;44(D1):D646–653. doi: 10.1093/nar/gkv1227
   PubMed Web of Science ®Google Scholar
• González-Medina M, Medina-Franco JL. Platform for Unified Molecular Analysis: PUMA. J Chem Inf Model. 2017;57(8):1735–1740. doi: 10.1021/acs.jcim.7b00253
   PubMed Web of Science ®Google Scholar
• Hughes J, Rees S, Kalindjian S, et al. Principles of early drug discovery. Br J Pharmacol. 2011;162(6):1239–1249. doi: 10.1111/j.1476-5381.2010.01127.x
   PubMed Web of Science ®Google Scholar
• Hernando-Amado S, Alcalde-Rico M, Gil-Gil T, et al. Naringenin inhibition of the Pseudomonas aeruginosa quorum sensing response is based on its time-dependent competition with N-(3-Oxo-dodecanoyl)-L-homoserine lactone for LasR binding. Front Mol Biosci. 2020;7:25. DOI:10.3389/fmolb.2020.00025
   PubMed Web of Science ®Google Scholar
• Pearlman DA, Case DA, Caldwell JW, et al. AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Comput Phys Commun. 1995;91(1–3):1–41. doi: 10.1016/0010-4655(95)00041-D
   Web of Science ®Google Scholar
• Miller BR, McGee TD, Swails JM, et al. Mmpbsa.Py: an efficient program for end-state free energy calculations. J Chem Theory Comput. 2012;8(9):3314–3321. doi: 10.1021/ct300418h
   PubMed Web of Science ®Google Scholar
• Kumari A, Mittal L, Srivastava M, et al. Binding mode characterization of 13b in the monomeric and dimeric states of SARS-CoV-2 main protease using molecular dynamics simulations. J Biomol Struct Dynamics. 2021;40(19):1–19. doi: 10.1080/07391102.2021.1927844
   Web of Science ®Google Scholar
• Mittal L, Srivastava M, Kumari A, et al. Interplay among structural stability, plasticity, and energetics determined by conformational attuning of flexible loops in PD-1. J Chem Inf Model. 2021;61(1):358–384. doi: 10.1021/acs.jcim.0c01080
   PubMed Web of Science ®Google Scholar
• Singh M, Srivastava M, Wakode SR, et al. Elucidation of structural determinants delineates the residues playing key roles in differential dynamics and selective inhibition of Sirt1–3. J Chem Inf Model. 2021;61(3):1105–1124. doi: 10.1021/acs.jcim.0c01193
   PubMed Web of Science ®Google Scholar
• Jeni LA, Cohn JF, De La Torre F Facing imbalanced data recommendations for the use of performance metrics. International Conference on Computational Intell Interact Workshops 2013, 245–251, doi:10.1109/acii.2013.47.
   Google Scholar
• Thabtah F, Hammoud S, Kamalov F, et al. Data imbalance in classification: experimental evaluation. Inf Sci. 2019;513:429–441. doi: 10.1016/j.ins.2019.11.004
   Web of Science ®Google Scholar
• Hakim Abdul Hamid M, Yusoff M, Mohamed A. Survey on highly imbalanced multi-class data. Int JAdv Comput Sci Appl (IJACSA). 2022;13(6). doi: 10.14569/IJACSA.2022.0130627
   Google Scholar
• Qiao Z, Li L, Li S, et al. Molecular fingerprint and machine learning to accelerate design of high‐performance homochiral metal–organic frameworks. AIChE J. 2021;67(10):e17352. doi: 10.1002/aic.17352
   Web of Science ®Google Scholar
• Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29–36. doi: 10.1148/radiology.143.1.7063747
   PubMed Web of Science ®Google Scholar
• Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta (BBA) - Protein Struct. 1975;405:442–451. doi: 10.1016/0005-2795(75)90109-9
   PubMed Web of Science ®Google Scholar
• Fourches D, Muratov E, Tropsha A. Curation of chemogenomics data. Nat Chem Biol. 2015;11(8):535–535. doi: 10.1038/nchembio.1881
   PubMed Web of Science ®Google Scholar
• Wong T, Yeh P. Reliable accuracy estimates from k-fold cross validation. IEEE Trans Knowledge Data Eng. 2020;32(8):1586–1594. doi: 10.1109/TKDE.2019.2912815
   Web of Science ®Google Scholar
• Afolabi LT, Saeed F, Hashim H, et al. Ensemble learning method for the prediction of new bioactive molecules. Plos One. 2018;13(1):e0189538. doi: 10.1371/journal.pone.0189538
   PubMed Web of Science ®Google Scholar
• Yu T-H, Su B-H, Battalora LC, et al. Ensemble modeling with machine learning and deep learning to provide interpretable generalized rules for classifying CNS drugs with high prediction power. Brief Bioinform. 2021;23(1). doi: 10.1093/bib/bbab377
   Web of Science ®Google Scholar
• Sarkar C, Das B, Rawat VS, et al. Artificial intelligence and machine learning technology driven modern drug discovery and development. Int J Mol Sci. 2023;24(3):2026. doi: 10.3390/ijms24032026
   PubMed Web of Science ®Google Scholar
• Geroldinger A, Lusa L, Nold M, et al. Leave-one-out cross-validation, penalization, and differential bias of some prediction model performance measures—a simulation study. Diagn Progn Res. 2023;7(1):9. doi: 10.1186/s41512-023-00146-0
   PubMedGoogle Scholar
• Sadiq S, Rana NF, Zahid MA, et al. Virtual screening of FDA-approved drugs against LasR of Pseudomonas aeruginosa for antibiofilm potential. Molecules. 2020;25(16):3723. doi: 10.3390/molecules25163723
   PubMedGoogle Scholar
• Vetrivel A, Ramasamy J, Natchimuthu S, et al. Combined machine learning and pharmacophore based virtual screening approaches to screen for antibiofilm inhibitors targeting LasR of Pseudomonas aeruginosa. J Biomol Struct Dynamics. 2022;41(9):1–19. doi: 10.1080/07391102.2022.2064331
   Web of Science ®Google Scholar
• Kalia M, Singh PK, Yadav VK, et al. Structure based virtual screening for identification of potential quorum sensing inhibitors against LasR master regulator in Pseudomonas aeruginosa. Microb Pathog. 2017;107:136–143. DOI:10.1016/j.micpath.2017.03.026
   PubMed Web of Science ®Google Scholar
• Yang L, Rybtke MT, Jakobsen TH, et al. Computer-aided identification of recognized drugs as Pseudomonas aeruginosa quorum-sensing inhibitors. Antimicrob Agents Chemother. 2009;53(6):2432–2443. doi: 10.1128/AAC.01283-08
   PubMed Web of Science ®Google Scholar
• Zhong L, Ravichandran V, Zhang N, et al. Attenuation of Pseudomonas aeruginosa quorum sensing by natural products: Virtual screening, evaluation and biomolecular interactions. Int J Mol Sci. 2020;21(6):2190. doi: 10.3390/ijms21062190
   PubMed Web of Science ®Google Scholar
• Shukla A, Shukla G, Parmar P, et al. Exemplifying the next generation of antibiotic susceptibility intensifiers of phytochemicals by LasR-mediated quorum sensing inhibition. Sci Rep. 2021;11(1):22421. doi: 10.1038/s41598-021-01845-8
   PubMed Web of Science ®Google Scholar
• Vadakkan K, Hemapriya J, Anbarasu A, et al. Quorum quenching by 2-Hydroxyanisole extracted from Solanum torvum on Pseudomonas aeruginosa and its inhibitory action upon LasR protein. Gene Rep. 2020;21:100802. doi: 10.1016/j.genrep.2020.100802
   Web of Science ®Google Scholar
• Luo J, Dong B, Wang K, et al. Baicalin inhibits biofilm formation, attenuates the quorum sensing-controlled virulence and enhances Pseudomonas aeruginosa clearance in a mouse peritoneal implant infection model. Plos One. 2017;12(4):e0176883. doi: 10.1371/journal.pone.0176883
   PubMed Web of Science ®Google Scholar
• Almalki AJ, Ibrahim TS, Elhady SS, et al. Computational and biological evaluation of β-adrenoreceptor blockers as promising bacterial anti-virulence agents. Pharmaceuticals (Basel). 2022;15(2):110. doi: 10.3390/ph15020110
   PubMedGoogle Scholar
• Elfaky MA, Elbaramawi SS, Eissa AG, et al. Drug repositioning: doxazosin attenuates the virulence factors and biofilm formation in Gram-negative bacteria. Appl Microbiol Biotechnol. 2023;107(11):3763–3778. doi: 10.1007/s00253-023-12522-3
   PubMed Web of Science ®Google Scholar
• Kaur H, Pannu HS, Malhi AK. A systematic review on imbalanced data challenges in machine learning: Applications and solutions. ACM Comput Surveys. 2019;52(4):1–36. doi: 10.1145/3343440
   Web of Science ®Google Scholar
• Ganganwar V. An overview of classification algorithms for imbalanced datasets. Int J Emerging Technol Adv Eng. 2012;2:42–47.
   Google Scholar
• Sapundzhi F, Prodanova K, Lazarova M Survey of the scoring functions for protein-ligand docking. AIP Conference Proceedings 2172, 100008, doi:10.1063/1.5133601 (2019).
   Google Scholar
• Pascolutti M, Quinn RJ. Natural products as lead structures: chemical transformations to create lead-like libraries. Drug Discovery Today. 2014;19:215–221. doi: 10.1016/j.drudis.2013.10.013
   PubMed Web of Science ®Google Scholar
• Teague SJ, Davis AM, Leeson PD, et al. The design of lead-like combinatorial libraries. Angew Chem Int Ed Engl. 1999;38:3743–3748. doi: 10.1002/(sici)1521-3773(19991216)38:24<3743:Aid-anie3743>3.0.Co;2-u
   PubMed Web of Science ®Google Scholar
• Magalhães RP, Vieira TF, Melo A, et al. Identification of novel candidates for inhibition of LasR, a quorum-sensing receptor of multidrug resistant Pseudomonas aeruginosa, through a specialized multi-level in silico approach. Mol Syst Des Eng. 2022;7(5):434–446. doi: 10.1039/D2ME00009A
   Web of Science ®Google Scholar
• Pereira LC, Fátima MAD, Santos VV, et al. Pharmacokinetic/Pharmacodynamic modeling and application in antibacterial and antifungal pharmacotherapy: A narrative review. Antibiotics. 2022;11(8):986. doi: 10.3390/antibiotics11080986
   Google Scholar
• Grzegorzewski J, Brandhorst J, Green K, et al. PK-DB: pharmacokinetics database for individualized and stratified computational modeling. Nucleic Acids Res. 2021;49(D1):D1358–d1364. doi: 10.1093/nar/gkaa990
   PubMed Web of Science ®Google Scholar