Strategies of Polypharmacology

Wang, Zhiguo; Yang, Baofeng

doi:10.1007/978-3-031-04998-9_2

Cited by 4 publications

(4 citation statements)

References 209 publications

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“…According to the literature, molecular modeling and molecular dynamics simulations are highly effective in reducing the number of potential FTO inhibitors while still being computationally efficient [83]. Drug virtual screening has a lengthy and extensive history, encompassing the development of various related techniques like docking, Quantitative Structure Activity Relationship (QSAR), pharmacophore, and structure-based ligand similarity [84]. The increasing availability of deep learning algorithms and the growing collection of experimental protein-ligand interaction data will significantly enhance the use of deep learning for virtual drug screening [85].…”

Section: Discussionmentioning

confidence: 99%

Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures

Mayuri,

Varalakshmi,

Tharaheswari

et al. 2024

BioMedInformatics

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The fat mass and obesity-associated (FTO) protein catalyzes metal-dependent modifications of nucleic acids, namely the demethylation of methyl adenosine inside mRNA molecules. The FTO protein has been identified as a potential target for developing anticancer therapies. Identifying a suitable ligand-targeting FTO protein is crucial to developing chemotherapeutic medicines to combat obesity and cancer. Scientists worldwide have employed many methodologies to discover a potent inhibitor for the FTO protein. This study uses deep learning-based methods and molecular docking techniques to investigate the FTO protein as a target. Our strategy involves systematically screening a database of small chemical compounds. By utilizing the crystal structures of the FTO complexed with ligands, we successfully identified three small-molecule chemical compounds (ZINC000003643476, ZINC000000517415, and ZINC000001562130) as inhibitors of the FTO protein. The identification process was accomplished by employing a combination of screening techniques, specifically deep learning (DeepBindGCN) and Autodock vina, on the ZINC database. These compounds were subjected to comprehensive analysis using 100 nanoseconds of molecular dynamics and binding free energy calculations. The findings of our study indicate the identification of three candidate inhibitors that might effectively target the human fat mass and obesity protein. The results of this study have the potential to facilitate the exploration of other chemicals that can interact with FTO. Conducting biochemical studies to evaluate these compounds’ effectiveness may contribute to improving fat mass and obesity treatment strategies.

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Section: Discussionmentioning

confidence: 99%

Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures

Mayuri,

Varalakshmi,

Tharaheswari

et al. 2024

BioMedInformatics

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“…88,95 Similarly, a 3D-crystal structure of receptor human topoisomerase II was also downloaded from RCSB databank (PDB ID: 5GWK; resolution: 3.15 Å). 45 We then carried out molecular docking simulations using the popular soware module, Glide, Schrodinger, LLC, NY, 2023. The 3D structures of synthesized analogues (ligands) were drawn using the soware 'MarvinSketch ® ' 5.8.3 (2012) and 'ChemAxon' tools.…”

Section: Molecular Docking Simulationsmentioning

confidence: 99%

“…The term "hybrid molecule" refers to the method used oen in drug design to increase the potency, selectivity, and overall pharmacological characteristics of drugs by combining two distinct pharmacophores inside a single molecular scaffold. 45,46 This strategy takes use of the pharmacophores' distinct characteristics to provide a synergistic impact, which may increase efficacy against a target or illness. 47 Coumarins are heterocyclic polyphenolic chemicals found throughout plants.…”

Section: Introductionmentioning

confidence: 99%

Novel 2-substituted-quinoxaline analogs with potential antiproliferative activity against breast cancer: insights into cell cycle arrest, topoisomerase II, and EGFR activity

Salem,

Abu El-ata,

Elsayed

et al. 2023

RSC Adv.

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“…Medicinal polypharmacology also stretches far into the very basis of drug design, discovery, and development itself (Anighoro et al, 2014;Proschak et al, 2019). Multitarget agents allow not only for addressing multiple druggable targets of the addressable "diseasome" (Wang & Yang, 2022); they also reach into a network of (yet) undruggable targets by either of two ways: (i) impact on a druggable target which subsequently reaches into a network of undruggable targets by a known or unknown pathway; or (ii) direct interaction with the to this point undruggable target itself (Korcsmaros et al, 2007). Multitarget agents are suitable ligands to deorphanize undruggable targets:…”

mentioning

confidence: 99%

Medicinal polypharmacology: Exploration and exploitation of the polypharmacolome in modern drug development

Stefan,

Rafehi

2023

Drug Development Research

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At the core of complex and multifactorial human diseases, such as cancer, metabolic syndrome, or neurodegeneration, are multiple players that cross‐talk in robust biological networks which are intrinsically resilient to alterations. These multifactorial diseases are characterized by sophisticated feedback mechanisms which manifest cellular imbalance and resistance to drug therapy. By adhering to the specificity paradigm (“one target‐one drug concept”), research focused for many years on drugs with very narrow mechanisms of action. This narrow focus promoted therapy ineffectiveness and resistance. However, modern drug discovery has evolved over the last years, increasingly emphasizing integral strategies for the development of clinically effective drugs. These integral strategies include the controlled engagement of multiple targets to overcome therapy resistance. Apart from the additive or even synergistic effects in therapy, multitarget drugs harbor molecular‐structural attributes to explore orphan targets of which intrinsic substrates/physiological role(s) and/or modulators are unknown for future therapy purposes. We designated this multidisciplinary and translational research field between medicinal chemistry, chemical biology, and molecular pharmacology as ‘medicinal polypharmacology’. Medicinal polypharmacology emerged as alternative approach to common single‐targeted pharmacology stretching from basic drug and target identification processes to clinical evaluation of multitarget drugs, and the exploration and exploitation of the ‘polypharmacolome’ is at the forefront of modern drug development research.

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Strategies of Polypharmacology

Cited by 4 publications

References 209 publications

Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures

Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures

Novel 2-substituted-quinoxaline analogs with potential antiproliferative activity against breast cancer: insights into cell cycle arrest, topoisomerase II, and EGFR activity

Medicinal polypharmacology: Exploration and exploitation of the polypharmacolome in modern drug development

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