2019
DOI: 10.2174/1871520618666181119110934
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Virtual Screening of Natural Products to Select Compounds with Potential Anticancer Activity

Abstract: Cancer is the main cause of death, so the search for active agents to be used in the therapy of this disease, is necessary. According to studies conducted, substances derived from natural products have shown to be promising in this endeavor. To these researches, one can associate with the aid of computational chemistry, which is increasingly gaining popularity, due to the possibility of developing alternative strategies that could help in choosing an appropriate set of compounds, avoiding unnecessary expenses … Show more

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Cited by 11 publications
(3 citation statements)
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“…Several natural bioactive compounds have been shown to possess anti-cancer activities, including tocotrienols (Aggarwal et al, 2019, Fu et al, 2019, Tung and Ng, 2019, genistein (Zhang et al, 2019, Chae et al, 2019 and others (Cavalcanti et al, 2019). Consuming some of these bioactive compounds may be beneficial to reduce risk of cancer and its mortality rate.…”
Section: A R T I C L E I N P R E S Smentioning
confidence: 99%
“…Several natural bioactive compounds have been shown to possess anti-cancer activities, including tocotrienols (Aggarwal et al, 2019, Fu et al, 2019, Tung and Ng, 2019, genistein (Zhang et al, 2019, Chae et al, 2019 and others (Cavalcanti et al, 2019). Consuming some of these bioactive compounds may be beneficial to reduce risk of cancer and its mortality rate.…”
Section: A R T I C L E I N P R E S Smentioning
confidence: 99%
“…Computational modeling plays a crucial role in anticancer drug discovery, including the identification and development of anticancer compounds. Some of the computational methods are used to develop natural products as anticancer agents, which involves the following: molecular docking for virtual screening and binding site validation [25], pharmacophore modeling to identify pharmacophores [26], quantitative structure-activity relationship (QSAR) modeling to predict activity and toxicity [27], molecular dynamics simulation to understand binding mode, affinity, and solvent effect [28], ADME property prediction [29], network pharmacology to construct and analyze networks of protein-protein interactions and pathways affected by natural compounds [30], and machine learning and artificial intelligence algorithm-based modeling to predict various ADMET properties and optimize natural compounds [31]. Computational modeling can significantly accelerate the drug discovery process, making it more cost-effective and efficient.…”
Section: Introductionmentioning
confidence: 99%
“…Virtaul screening and predicting drugs target using bioinformatics algorithm is an important tool for discovery and development of new drugs based on their molecular structure [43,44] [45]. It is widely applied for hypothesize, evaluation of affinity with receptor-ligand interaction, optimization of ligand molecule, pharmacophore identification, development of computational drug model and molecular docking studies.…”
Section: Introductionmentioning
confidence: 99%