Exploring the Relationship between PheSA Scores and Ligand Efficiency in the Discovery of Potent Antimalarials: A Computational Perspective
DOI:
https://doi.org/10.25026/jtpc.v8i1.609Keywords:
PheSA, Cycloguanil Analogues, Ligand Efficiency, Antimalarial drugsAbstract
Pharmacophore Enhanced Shape Alignment (PheSA) compares the similarities of compounds based on their pharmacophoric and geometrical characteristics. Ligand efficiency is a notion used to maximize the potency and effectiveness of medication candidates by taking into account their molecular weight and binding affinity. This study mainly focused on Cycloguanil analogues to evaluate the association between PheSA scores and ligand efficiency in the identification of effective antimalarials. Information on 36 PfDHFR inhibitors, their structures and biological activity was retrieved from the ChEMBL database. Based on shape and pharmacophore similarity, the PheSA algorithm was used to compare the 3D structures of the inhibitors. Based on a de novo synthesis method, 257 new compounds with greater PheSA similarity scores that have a striking resemblance to cycloguanil were created. The PheSA score and ligand efficiency have a moderately positive link (correlation coefficient of 0.675) according to the analysis. However, the virtual screening of cycloguanil analogues based on PheSA similarity scores offers a useful initial evaluation of structural similarity, directing further experimental studies to find interesting substances for the creation of effective antimalarial drug.
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References
Trigg PI., Kondrachine AV. 1998. Commentary: Malaria control in the 1990s. Bulletin of the World Health Organization, 76(1), 11-16.
Wells TN., Alonso PL., Gutteridge WE. 2009. New medicines to improve control and contribute to the eradication of malaria. Nature Reviews Drug Discovery, 8(11), 879-891.
Na?Bangchang K., Karbwang J. 2009. Current status of malaria chemotherapy and the role of pharmacology in antimalarial drug research and development. Fundamental and Clinical Pharmacology, 23(4), 387-409.
Patel AR., Patel HB., Mody SK., Singh RD., Sarvaiya VN., Vaghela SH., Tukra S. 2021. Virtual screening in drug discovery. Journal of Veterinary Pharmacology and Toxicology, 20(2), 1-9.
Cavalli A., Poluzzi E., De Ponti F., Recanatini M. 2002. Toward a pharmacophore for drugs inducing the long QT syndrome: Insights from a CoMFA study of HERG K+ channel blockers. Journal of Medicinal Chemistry, 45(18), 3844-3853.
Muegge I., Mukherjee, P. 2016. An overview of molecular fingerprint similarity search in virtual screening. Expert Opinion on Drug Discovery, 11(2), 137-148.
Hopkins AL., Keserü GM., Leeson PD., Rees DC., Reynolds CH. 2014. The role of ligand efficiency metrics in drug discovery. Nature Reviews Drug Discovery, 13(2), 105-121.
Schultes S., de Graaf C., Haaksma EE., de Esch IJ., Leurs R., Krämer O. 2010. Ligand efficiency as a guide in fragment hit selection and optimization. Drug Discovery Today: Technologies, 7(3), 157-162.
Rastelli G., Pacchioni S., Sirawaraporn W., Sirawaraporn R., Parenti MD., Ferrari, AM. 2003. Docking and database screening reveal new classes of Plasmodium falciparum dihydrofolate reductase inhibitors. Journal of Medicinal Chemistry, 46(14), 2834-2845.
Evbuomwan IO., Alejolowo OO., Elebiyo TC., Nwonuma CO., Ojo OA., Edosomwan E. U., Oluba, OM. 2023. In silico modeling revealed phytomolecules derived from Cymbopogon citratus (DC.) leaf extract as promising candidates for malaria therapy. Journal of Biomolecular Structure and Dynamics, 1-18.
Gupta R., Srivastava D., Sahu M., Tiwari S., Ambasta RK., Kumar P. 2021. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery. Molecular Diversity, 25, 1315-1360.
El-Shamy NT., Alkaoud AM., Hussein RK., Ibrahim MA., Alhamzani AG., Abou-Krisha, MM. 2022. DFT, ADMET and Molecular Docking Investigations for the Antimicrobial Activity of 6, 6?-Diamino-1, 1?, 3, 3?-tetramethyl-5, 5?-(4-chlorobenzylidene) bis [pyrimidine-2, 4 (1H, 3H)-dione]. Molecules, 27(3), 620.
Tijjani H., Olatunde A., Adegunloye AP., Ishola, AA. 2022. In silico insight into the interaction of 4-aminoquinolines with selected SARS-CoV-2 structural and nonstructural proteins in Coronavirus Drug Discovery (pp. 313-333). Elsevier.
Abad-Zapatero C. 2007. Ligand efficiency indices for effective drug discovery. Expert Opinion on Drug Discovery, 2(4), 469-488.
Murray CW., Carr MG., Callaghan O., Chessari G., Congreve M., Cowan, S. 2010. Fragment-based drug discovery applied to Hsp90. Discovery of two lead series with high ligand efficiency. Journal of Medicinal Chemistry, 53(16), 5942-5955.
Copeland RA., Pompliano DL., Meek, T. D. 2006. Drug–target residence time and its implications for lead optimization. Nature Reviews Drug Discovery, 5(9), 730-739.
Duarte AR., Cadoni E., Ressurreição AS., Moreira R., Paulo, A. 2018. Design of Modular G?quadruplex Ligands. ChemMedChem, 13(9), 869-893.
Li GB., Yang LL., Wang WJ., Li LL., Yang SY. 2013. ID-Score: A new empirical scoring function based on a comprehensive set of descriptors related to protein–ligand interactions. Journal of Chemical Information and Modeling, 53(3), 592-600.
Kilina S., Ivanov S., Tretiak S. 2009. Effect of surface ligands on optical and electronic spectra of semiconductor nanoclusters. Journal of the American Chemical Society, 131(22), 7717-7726.
Sliwoski G., Kothiwale S., Meiler J., Lowe EW. 2014. Computational methods in drug discovery. Pharmacological reviews, 66(1), 334-395.
Coghlan A., Padalino G., O'Boyle, NM., Hoffmann KF., Berriman M. 2022. Identification of anti-schistosomal, anthelmintic and anti-parasitic compounds curated and text- mined from the scientific literature. Wellcome Open Research, 7.
Fedorov RG., Maletti S., Heubner C., Michaelis A., Ein?Eli Y. 2021. Molecular Engineering Approaches to Fabricate Artificial Solid?Electrolyte Interphases on Anodes for Li?Ion Batteries: A Critical Review. Advanced Energy Materials, 11(26), 2101173.
Sruthi CK., Balaram H., Prakash MK. 2020. Toward developing intuitive rules for protein variant effect prediction using deep mutational scanning data. ACS Omega, 5(46), 29667-29677.
Ragoza M., Masuda T., Koes DR. 2022. Generating 3D molecules conditional on receptor binding sites with deep generative models. Chemical Science, 13(9), 2701-2713.
Ursu O., Rayan A., Goldblum A., Oprea TI. 2011. Understanding drug?likeness. Wiley Interdisciplinary Reviews: Computational Molecular Science, 1(5), 760-781.
Daina A., Michielin O., Zoete V. 2017. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7(1), 42717.
Chillistone S., Hardman JG. 2017. Factors affecting drug absorption and distribution. Anaesthesia & Intensive Care Medicine, 18(7), 335-339.
Stegemann S., Leveiller F., Franchi D., De Jong H., Lindén, H. 2007. When poor solubility becomes an issue: From early stage to proof of concept. European Journal of Pharmaceutical Sciences, 31(5), 249-261.
Corral MG., Leroux J., Stubbs KA., Mylne JS. 2017. Herbicidal properties of antimalarial drugs. Scientific Reports, 7(1), 45871.
Ferenczy GG., Keser? GM. 2016. Ligand efficiency metrics and their use in fragment optimizations. Fragment?based Drug Discovery Lessons and Outlook, 75-98.
Leeson PD., Young RJ. 2015. Molecular property design: Does everyone get it?. ACS Medicinal Chemistry Letters, 6(7), 722-725.
Sastry GM., Dixon SL., Sherman W. 2011. Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring. Journal of Chemical Information and Modeling, 51(10), 2455-2466.
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