Virtual Screening Techniques and Current Computational Infrastructures

Haga, Jason; Ichikawa, Kohei; Date, Susumu

doi:10.2174/1381612822666160414142530

Cited by 34 publications

(19 citation statements)

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“…Ten of these compounds were identified as active, resulting in a screening hit rate of 16.13% (10/62). The virtual screening used in this study is an important example of the application of computer-aided drug design, including database searches based on pharmacophore models, QSAR models, structural similarity, molecular docking, pharmacokinetic properties and so on (Haga et al, 2016). These methods use computational methods and professional software to support hit finding and lead optimization in drug discovery.…”

Section: Discussionmentioning

confidence: 99%

Pharmacophore-Based Virtual Screening Toward the Discovery of Novel Anti-echinococcal Compounds

Liu

Yin

Yao

et al. 2020

Front. Cell. Infect. Microbiol.

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Echinococcosis is a serious helminthic zoonosis with a great impact on human health and livestock husbandry. However, the clinically used drugs (benzimidazoles) have a low cure rate, so alternative drugs are urgently needed. Currently, drug screenings for echinococcosis are mainly phenotype-based, and the efficiency of identifying active compounds is very low. With a pharmacophore model generated from the structures of active amino alcohols, we performed a virtual screening to discover novel compounds with anti-echinococcal activity. Sixty-two compounds from the virtual screening were tested on Echinococcus multilocularis protoscoleces, and 10 of these compounds were found to be active. After further evaluation of their cytotoxicity, S6 was selected along with two active amino alcohols for in vivo pharmacodynamic and pharmacokinetic studies. At the two tested doses (50 and 25 mg/kg), S6 inhibited the growth of E. multilocularis in mice (14.43 and 9.53%), but no significant difference between the treatment groups and control group was observed. Treatment with BTB4 and HT3 was shown to be ineffective. During the 28 days of treatment, the death of mice in the mebendazole, HT3, and BTB4 groups indicated their toxicity. The plasma concentration of S6 administered by both methods was very low, with the C max being only 1 ng/ml after oral administration and below the detection limit after intramuscular administration. In addition, the plasma concentrations of BTB4 and HT3 in vitro did not reach high enough levels to kill the parasites. The toxicities of these two amino alcohols indicated that they are not suitable for further development as anti-echinococcal drugs. However, further attempts should be made to increase the bioavailability of S6 and modify its structure. In this study, we demonstrate that pharmacophore-based virtual screenings with high drug identification efficiency could be used to find novel drugs for treating echinococcosis.

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Section: Discussionmentioning

confidence: 99%

Pharmacophore-Based Virtual Screening Toward the Discovery of Novel Anti-echinococcal Compounds

Liu

Yin

Yao

et al. 2020

Front. Cell. Infect. Microbiol.

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“…Virtual screening has become an important part of the drug discovery process. Virtual screening methods and VS case studies have been reviewed elsewhere [2,3,4,5,6,7,8,9,10,11]. In this review, we summarize, based on our experience, the most commonly used VS methodologies and discuss their strengths and weaknesses in order to both introduce the inexperienced researcher to new methodologies and advise the experienced researcher on how to prevent common mistakes and the improper usage of VS methodologies.…”

Section: Virtual Screeningmentioning

confidence: 99%

The Light and Dark Sides of Virtual Screening: What Is There to Know?

Gimeno

Ojeda-Montes

Tomás-Hernández

et al. 2019

IJMS

186

147

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Virtual screening consists of using computational tools to predict potentially bioactive compounds from files containing large libraries of small molecules. Virtual screening is becoming increasingly popular in the field of drug discovery as in silico techniques are continuously being developed, improved, and made available. As most of these techniques are easy to use, both private and public organizations apply virtual screening methodologies to save resources in the laboratory. However, it is often the case that the techniques implemented in virtual screening workflows are restricted to those that the research team knows. Moreover, although the software is often easy to use, each methodology has a series of drawbacks that should be avoided so that false results or artifacts are not produced. Here, we review the most common methodologies used in virtual screening workflows in order to both introduce the inexperienced researcher to new methodologies and advise the experienced researcher on how to prevent common mistakes and the improper usage of virtual screening methodologies.

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“…In this context, computational methods have been widely applied to the virtual screening of small molecules with potential use as inhibitors (or agonists), which constitutes one of the initial steps for drug discovery and rational drug design [2, 3]. Molecular docking methods, for instance, can predict both the geometry of a protein-ligand complex and its corresponding binding energy [4].…”

Section: Introductionmentioning

confidence: 99%

DINC 2.0: A New Protein–Peptide Docking Webserver Using an Incremental Approach

et al. 2017

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Molecular docking is a standard computational approach to predict binding modes of protein-ligand complexes, by exploring alternative orientations and conformations of the ligand (i.e., by exploring ligand flexibility). Docking tools are largely used for virtual screening of small drug-like molecules, but their accuracy and efficiency greatly decays for ligands with more than 10 flexible bonds. This prevents a broader use of these tools to dock larger ligands such as peptides, which are molecules of growing interest in cancer research. To overcome this limitation, our group has previously proposed a meta-docking strategy, called DINC, to predict binding modes of large ligands. By incrementally docking overlapping fragments of a ligand, DINC allowed predicting binding modes of peptide-based inhibitors of transcription factors involved in cancer. Here we describe DINC 2.0, a revamped version of the DINC webserver with enhanced capabilities and a more user-friendly interface. DINC 2.0 allows docking ligands that were previously too challenging for DINC, such as peptides with more than 25 flexible bonds. The webserver is freely accessible at http://dinc.kavrakilab.org, together with additional documentation and video tutorials. Our team will provide continuous support for this tool and is working on extending its applicability to other challenging fields, such as personalized immunotherapy against cancer.

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Virtual Screening Techniques and Current Computational Infrastructures

Cited by 34 publications

References 0 publications

Pharmacophore-Based Virtual Screening Toward the Discovery of Novel Anti-echinococcal Compounds

Pharmacophore-Based Virtual Screening Toward the Discovery of Novel Anti-echinococcal Compounds

The Light and Dark Sides of Virtual Screening: What Is There to Know?

DINC 2.0: A New Protein–Peptide Docking Webserver Using an Incremental Approach

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