Systematic review on role of structure based drug design (SBDD) in the identification of anti-viral leads against SARS-Cov-2

Bajad, Nilesh Gajanan; Swetha, Rayala; Gutti, Gopichand; Sharma, Anjali; Singh, Meenakshi; Kumar, Ashok; Singh, Sushil Kumar

doi:10.1016/j.crphar.2021.100026

Cited by 29 publications

(15 citation statements)

References 112 publications

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“…Similarly, Hydrocortisone use in patients with severe sepsis has been shown to significantly decrease IL-1β, interferon-γ (IFN-γ), TNF-α, and IL-6 levels [109] . Methylprednisolone has been reported to have some benefit in acute lung injury [110] . In addition, due to their immunosuppressive activity, glucocorticoids could also delay virus clearance in COVID-19 patients as they did in SASR and MERS patients [111] .…”

Section: Current Pharmacological Therapies Against Covid-19mentioning

confidence: 99%

“…Artificial intelligence-based drug design is a newly emerged approach that has been recently used to discover novel drugs based on machine and deep and reinforcement learning algorithms [108] , [109] . Combining these drug design approaches with virtual drug screening and high-throughput screening approaches could greatly aid in the process of developing novel lead compounds for COVID-19 therapy [110] . Nevertheless, developing a new drug could take years, and quicker approaches must be taken to combat this devastating pandemic.…”

Section: Prospective Of Drug Development Against Covid-19mentioning

confidence: 99%

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Pharmacological therapies and drug development targeting SARS-CoV-2 infection

Jiang

Rubin²,

Zhou³

et al. 2022

Cytokine & Growth Factor Reviews

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Section: Current Pharmacological Therapies Against Covid-19mentioning

confidence: 99%

Section: Prospective Of Drug Development Against Covid-19mentioning

confidence: 99%

Pharmacological therapies and drug development targeting SARS-CoV-2 infection

Jiang

Rubin²,

Zhou³

et al. 2022

Cytokine & Growth Factor Reviews

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“…This is largely owing to the fact that conventional drug discovery pipelines are challenged by a significant demand for labor and time, as well as by rising costs (19). Computational drug design can effectively lower these costs and accelerate the process of the development or repurposing of drugs; time is a crucial factor in the case of viral outbreaks, as evidenced by the current COVID-19 pandemic and the frantic search for therapeutic targets and effective drugs against SARS-CoV-2 (20,21).…”

Section: Structure-based Drug Designmentioning

confidence: 99%

Novel computational pipelines in antiviral structure‑based drug design (Review)

et al. 2022

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Viral infections constitute a fundamental and continuous challenge for the global scientific and medical community, as highlighted by the ongoing COVID-19 pandemic. In combination with prophylactic vaccines, the development of safe and effective antiviral drugs remains a pressing need for the effective management of rare and common pathogenic viruses. The design of potent antivirals can be informed by the study of the three-dimensional structure of viral protein targets. Structure-based design of antivirals in silico provides a solution to the arduous and costly process of conventional drug development pipelines. Furthermore, rapid advances in high-throughput computing, along with the growth of available biomolecular and biochemical data, enable the development of novel computational pipelines in the hunt of antivirals. The incorporation of modern methods, such as deep-learning and artificial intelligence, has the potential to revolutionize the structure-based design and repurposing of antiviral compounds, with minimal side effects and high efficacy. The present review aims to provide an outline of both traditional computational drug design and emerging, high-level computing strategies. Contents 1. Introduction 2. Structure-based drug design 3. Novel computational pipelines 4. Conclusions

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“…However, the traditional experimental drug discovery is usually expensive and time-consuming. Structure-based drug design (SBDD) is a proven highly effective and economical computer-aided design approach that could speed up the drug discovery for SARS-CoV-2 [8] , [9] , [10] . A typical SBDD starts from the input protein sequence, builds a three-dimensional (3D) structure by structure biology or structure prediction, identifies binding sites, discovers active modulators through virtual screening or de novo design, predicts the protein-protein/ligand docking sites with high accuracy, and lastly simulates the dynamics evolution of the macromolecule ( Figure 2 ) [11] , [12] .…”

Section: Introductionmentioning

confidence: 99%