Molecular Architect: A User-Friendly Workflow for Virtual Screening

Maia, Eduardo Habib Bechelane; Medaglia, Lucas Rolim; Silva, Alisson Marques da; Taranto, Alex Gutterres

doi:10.1021/acsomega.9b04403

Cited by 33 publications

(28 citation statements)

References 72 publications

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“…In order to enhance the success rate of this virtual screening (VS) campaign, 2D approaches were discarded due to their intrinsic limitations and 3D methods were employed since they are more sensible and retain information [42]. Furthermore, a consensus analysis was performed in each step (LB and SB) to select the most promising compounds [43].…”

Section: Resultsmentioning

confidence: 99%

Ligand and structure-based Virtual Screening Applied to the SARS-CoV-2 Main Protease: an In Silico Repurposing Study

et al. 2020

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Aim: The identification of drugs for the coronavirus disease-19 pandemic remains urgent. In this manner, drug repurposing is a suitable strategy, saving resources and time normally spent during regular drug discovery frameworks. Essential for viral replication, the main protease has been explored as a promising target for the drug discovery process. Materials & methods: Our virtual screening pipeline relies on the known 3D properties of noncovalent ligands and features of crystalized complexes, applying consensus analyses in each step. Results: Two oral (bedaquiline and glibenclamide) and one buccal drug (miconazole) presented 3D similarity to known ligands, reasonable predicted binding modes and micromolar predicted binding affinity values. Conclusion: We identified three approved drugs as promising inhibitors of the main viral protease and suggested design insights for future studies for development of novel selective inhibitors.

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Section: Resultsmentioning

confidence: 99%

Ligand and structure-based Virtual Screening Applied to the SARS-CoV-2 Main Protease: an In Silico Repurposing Study

et al. 2020

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“…As such, we recommend that future, more focused, research continue our docking simulations by including target baits (to reflect the limitations of false-positive and false-negative results), considering solvent effects, flexible docking, and comparing multiple docking tools. To this end, we indicate the robust docking methodologies employed in [ 68 , 81 , 82 , 83 ].…”

Section: Discussionmentioning

confidence: 99%

Uncovering New Drug Properties in Target-Based Drug–Drug Similarity Networks

et al. 2020

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Despite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach—based on knowledge about the chemical structures—can not fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug–target interactions to infer drug properties. To this end, we define drug similarity based on drug–target interactions and build a weighted Drug–Drug Similarity Network according to the drug–drug similarity relationships. Using an energy-model network layout, we generate drug communities associated with specific, dominant drug properties. DrugBank confirms the properties of 59.52% of the drugs in these communities, and 26.98% are existing drug repositioning hints we reconstruct with our DDSN approach. The remaining 13.49% of the drugs seem not to match the dominant pharmacologic property; thus, we consider them potential drug repurposing hints. The resources required to test all these repurposing hints are considerable. Therefore we introduce a mechanism of prioritization based on the betweenness/degree node centrality. Using betweenness/degree as an indicator of drug repurposing potential, we select Azelaic acid and Meprobamate as a possible antineoplastic and antifungal, respectively. Finally, we use a test procedure based on molecular docking to analyze Azelaic acid and Meprobamate’s repurposing.

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“…40,41 As the enzymes were prepared, the molecular docking protocol was started. Thus, to evaluate the ability of the algorithm to predict possible ligand orientations, re-docking calculations were performed using the MolAr software, 42 with the implementation of the AutoDock Vina program. 43 As such, the values extracted from RMSD (5R82 = 0.94 Ǻ / 3H5Y = 1.55 Ǻ) indicated that Vina was able to predict the conformation that the co-crystallized ligands adopted experimentally within the SARS-CoV-2 M pro active site and SARS-CoV-2 RdRp polymerase.…”

Section: CLmentioning

confidence: 99%

“…56 The molecular docking was conducted with the tool AutoDock Vina (version 1.1.2), 43 as implemented in the MolAr (Molecular Architecture) software. 42 For the crystallographic M pro and RdRp polymerase structures preparation, the loop regions were rebuilt using the Modeller. 57 As such, the ions and water molecules were removed from the original PDB, with the exception of water molecules that were in the M pro and RdRp active sites.…”

Section: CLmentioning

confidence: 99%

Theoretical Insights into the Effect of Halogenated Substituent on the Electronic Structure and Spectroscopic Properties of the Favipiravir Tautomeric Forms and Its Implications on the Treatment of COVID-19

Assis¹,

Castro²,

Jesus³

et al. 2020

Preprint

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<p>In this study, we systematically investigated the electronic structure, spectroscopic (nuclear magnetic resonance, infrared, Raman, electron ionization mass spectrometry, UV-Vis, circular dichroism, and emission) properties, and tautomerism of halogenated favipiravir compounds (fluorine, chlorine, and bromine) from a computational perspective. Additionally, the effects of hydration on the proton transfer mechanism of the tautomeric forms of the halogenated favipiravir compounds are discussed. Our results suggest that spectroscopic properties allow for the elucidation of such tautomeric forms. As is well-known, the favipiravir compound has excellent antiviral properties and hence was recently tested for the treatment of new coronavirus (SARS-CoV-2). Through in silico modeling, in the current study, we evaluate the role of such tautomeric forms in order to consider the effect of drug-metabolism into the inhibition process of the main protease (M<sup>pro</sup>) and RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 virus. These findings clearly indicated that all title compounds are better as RNA-inhibiting.</p>

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Molecular Architect: A User-Friendly Workflow for Virtual Screening

Cited by 33 publications

References 72 publications

Ligand and structure-based Virtual Screening Applied to the SARS-CoV-2 Main Protease: an In Silico Repurposing Study

Ligand and structure-based Virtual Screening Applied to the SARS-CoV-2 Main Protease: an In Silico Repurposing Study

Uncovering New Drug Properties in Target-Based Drug–Drug Similarity Networks

Theoretical Insights into the Effect of Halogenated Substituent on the Electronic Structure and Spectroscopic Properties of the Favipiravir Tautomeric Forms and Its Implications on the Treatment of COVID-19

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