Editorial: In silico Methods for Drug Design and Discovery

Brogi, Simone; Ramalho, Teodorico C.; Kuča, Kamil; Medina-Franco, José L.; Valko, Marián

doi:10.3389/fchem.2020.00612

Cited by 208 publications

(141 citation statements)

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“…For this reason, the pursuit of safe and effective therapeutics against the emerging SARS-CoV-2 virus is critically important and urgent in the global fight against COVID- 19. In silico approaches play a crucial role in accelerating the research and development processes of new potential pharmaceutical drugs. 20…”

Section: Introductionmentioning

confidence: 99%

Covalent and non-covalent binding free energy calculations for peptidomimetic inhibitors of SARS-CoV-2 main protease

Awoonor-Williams

Abu‐Saleh

2021

Phys. Chem. Chem. Phys.

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Section: Introductionmentioning

confidence: 99%

Covalent and non-covalent binding free energy calculations for peptidomimetic inhibitors of SARS-CoV-2 main protease

Awoonor-Williams

Abu‐Saleh

2021

Phys. Chem. Chem. Phys.

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“…Computer-aided drug designs (CADD) are playing an increasing role in drug discovery and are critical in the costeffective identification of promising drug candidates [5]. Molecular docking employed for predicting the interactions between receptor and ligands is an integral aspect in drug discovery.…”

Section: Introductionmentioning

confidence: 99%

Anti-diabetic drug discovery using bioactive compounds: Molecular docking insights

Teibo¹,

Bello²,

Adebisi³

et al. 2021

GSC Biol. and Pharm. Sci.

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Diabetes mellitus is a metabolic disorder that has become a global health problem. About 500 million people were estimated to be living with diabetes in 2018 with about 20 million in Africa and 2 million cases in Nigeria. Bioactive compounds offer an advanced starting point in the search for highly specific and potent modulator of bimolecular function as well as novel drugs, which can be studied with more precision by using computer aided drug design (CADD). Molecular docking employed for predicting the interactions between receptor and ligands is an integral aspect in drug discovery. The main objective is to attain ligand-receptor complex with optimized conformation and with the intention of possessing less binding free energy. Several studies have used this method to explore the potency of bioactive compounds to predict better alternatives in the search for an anti-diabetic drug with very effective therapeutic role and minimal side effects. This has been carried out by using several compounds such as Quercetin, against endogenous targets such as Glycogen phosphorylase, Peroxisome Proliferator-activated Receptor (PPAR)-y, Glucokinase, Protein Tyrosine Phosphatase 1-beta (PTP-1B), GLUT4, etc. In Silico tools such as Protein Database (PDB), GenBank and softwares such as Autodock and modeller are of major importance to these studies. The paper seeks to examine bioactive compounds basically quercetin that have been successfully identified through molecular docking and their molecular targets as well as recent advances in the use of molecular docking in the novel discovery and explanation of mechanisms of actions of some bioactive compounds in anti-diabetic drug discovery.

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“…In silico approaches play a crucial role in accelerating the research and development processes of new potential pharmaceutical drugs. 20…”

Section: Introductionmentioning

confidence: 99%

Covalent and Non-Covalent Binding Free Energy Calculations for Peptidomimetic Inhibitors of SARS-CoV-2 Main Protease

Awoonor-Williams

Abu‐Saleh²

2020

Preprint

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COVID-19, the disease caused by the newly discovered coronavirus — SARS-CoV-2, has created global health, social, and economic crisis. At the time of writing (November 12, 2020), there are over 50 million confirmed cases and more than 1 million reported deaths due to COVID-19. Currently, there are no approved vaccines, and recently Veklury (remdesivir) was approved for the treatment of COVID-19 requiring hospitalization. The main protease (Mpro) of the virus is an attractive target for the development of effective antiviral therapeutics because it is required for proteolytic cleavage of viral polyproteins. Furthermore, the Mpro has no human homologues, so drugs designed to bind to this target directly have less risk for off-target reactivity. Recently, several high-resolution crystallographic structures of the Mpro in complex with inhibitors have been determined — to guide drug development and to spur efforts in structure-based drug design. One of the primary objectives of modern structure-based drug design is the accurate prediction of receptor-ligand binding affinities for rational drug design and discovery. Here, we perform rigorous alchemical absolute binding free energy calculations and QM/MM calculations to give insight into the total binding energy of two recently crystallized inhibitors of SARS-CoV-2 Mpro, namely, N3 and α-ketoamide 13b. The total binding energy consists of both covalent and non-covalent binding components since both compounds are covalent inhibitors of the Mpro. Our results indicate that the covalent and non-covalent binding free energy contributions of both inhibitors to the Mpro target differ significantly. The N3 inhibitor has more favourable non-covalent interactions, particularly hydrogen bonding, in the binding site of the Mpro than the α-ketoamide inhibitor. But the Gibbs energy of reaction for the Mpro–α-ketoamide covalent adduct is greater than the Gibbs reaction energy for the Mpro–N3 covalent adduct. These differences in the covalent and non-covalent binding free energy contributions for both inhibitors could be a plausible explanation for their in vitro differences in antiviral activity. Our findings highlight the importance of both covalent and non-covalent binding free energy contributions to the absolute binding affinity of a covalent inhibitor towards its target.

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Editorial: In silico Methods for Drug Design and Discovery

Cited by 208 publications

References 0 publications

Covalent and non-covalent binding free energy calculations for peptidomimetic inhibitors of SARS-CoV-2 main protease

Covalent and non-covalent binding free energy calculations for peptidomimetic inhibitors of SARS-CoV-2 main protease

Anti-diabetic drug discovery using bioactive compounds: Molecular docking insights

Covalent and Non-Covalent Binding Free Energy Calculations for Peptidomimetic Inhibitors of SARS-CoV-2 Main Protease

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