Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives

Romano, Joseph D.; Tatonetti, Nicholas P.

doi:10.3389/fgene.2019.00368

Cited by 117 publications

(79 citation statements)

References 163 publications

(173 reference statements)

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“…Many clinical trials and efforts are being made to provide potential therapies (ECT Register, 2020). Any traditional drug discovery program is a costly and timeconsuming process that sometimes takes decades to complete (Romano & Tatonetti, 2019). Thus, we cannot afford to wait for the discovery of medicines for Covid-19 from traditional drug discovery.…”

Section: Introductionmentioning

confidence: 99%

Peptide-like and small-molecule inhibitors against Covid-19

Pant

Singh

Ravichandiran

et al. 2020

Journal of Biomolecular Structure and Dynamics

270

225

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Coronavirus disease strain (SARS-CoV-2) was discovered in 2019, and it is spreading very fast around the world causing the disease Covid-19. Currently, more than 1.6 million individuals are infected, and several thousand are dead across the globe because of Covid-19. Here, we utilized the in-silico approaches to identify possible protease inhibitors against SARS-CoV-2. Potential compounds were screened from the CHEMBL database, ZINC database, FDA approved drugs and molecules under clinical trials. Our study is based on 6Y2F and 6W63 co-crystallized structures available in the protein data bank (PDB). Seven hundred compounds from ZINC/CHEMBL databases and fourteen hundred compounds from drug-bank were selected based on positive interactions with the reported binding site. All the selected compounds were subjected to standard-precision (SP) and extra-precision (XP) mode of docking. Generated docked poses were carefully visualized for known interactions within the binding site. Molecular mechanics-generalized born surface area (MM-GBSA) calculations were performed to screen the best compounds based on docking scores and binding energy values. Molecular dynamics (MD) simulations were carried out on four selected compounds from the CHEMBL database to validate the stability and interactions. MD simulations were also performed on the PDB structure 6YF2F to understand the differences between screened molecules and co-crystallized ligand. We screened 300 potential compounds from various databases, and 66 potential compounds from FDA approved drugs. Cobicistat, ritonavir, lopinavir, and darunavir are in the top screened molecules from FDA approved drugs. The screened drugs and molecules may be helpful in fighting with SARS-CoV-2 after further studies. ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

Peptide-like and small-molecule inhibitors against Covid-19

Pant

Singh

Ravichandiran

et al. 2020

Journal of Biomolecular Structure and Dynamics

270

225

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“…These studies utilize bioinformatics techniques and can be used to discover how candidate drugs cause therapeutic activity by predicting interactions between drugs and proteins, and analysis of influence on biological pathways and functions. 23 …”

Section: Introductionmentioning

confidence: 99%

Nigella sativa L as a potential phytotherapy for coronavirus disease 2019: A mini review of in silico studies

Koshak¹,

Koshak²

2020

Current Therapeutic Research

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Background Coronaviruses are responsible for several human diseases, such as the infectious novel coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nigella sativa is a natural food supplement with a known safety profile that may provide a wealth of documented antiviral compounds. Objective To explore the studies supporting the N sativa potential for hitting SARS-CoV-2 targets. Methods A literature search for published or preprint in silico studies between 1990 and 2020 in electronic databases (PubMed, Science Direct, Scopus, and Google Scholar) was performed for the terms Nigella sativa, black seed, coronavirus, SARS-CoV-2 , and COVID-19 . Results At least 8 in silico studies have shown that some compounds of N sativa , including nigelledine, α-hederin, hederagenin, thymohydroquinone, and thymoquinone, had high to moderate affinity with SARS-CoV-2 enzymes and proteins. These compounds may potentially inhibit SARS-CoV-2 replication and attachment to host cell receptors . Conclusions These preliminary data of in silico studies propose N sativa as a potential phytotherapy candidate for COVID-19. Further preclinical experimental evidence is required followed by a Phase I clinical trial. ( Curr Ther Res Clin Exp . 2020; 81:XXX–XXX)

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“…Using such approaches can reduce the overall cost of commercialization and also bridge the gap between drug discovery and availability. In comparison to the traditional drug repositioning, which relies on clinical discoveries, computational drug repositioning methods can reduce the drug development timeline [ 4 – 6 ].…”

Section: Introductionmentioning

confidence: 99%

RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

et al. 2020

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Background It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time. Methods In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease. Results The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II. Conclusion Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen.

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Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives

Cited by 117 publications

References 163 publications

Peptide-like and small-molecule inhibitors against Covid-19

Peptide-like and small-molecule inhibitors against Covid-19

Nigella sativa L as a potential phytotherapy for coronavirus disease 2019: A mini review of in silico studies

RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

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