Development of a Virtual Screening Method for Identification of “Frequent Hitters” in Compound Libraries

Background: 3C-like protease also called the main protease is an essential enzyme for the completion of the life cycle of Middle East Respiratory Syndrome Coronavirus. In our study we predicted compounds which are capable of inhibiting 3C-like protease, and thus inhibit the lifecycle of Middle East Respiratory Syndrome Coronavirus using in silico methods. </P><P> Methods: Lead like compounds and drug molecules which are capable of inhibiting 3C-like protease was identified by structure-based virtual screening and ligand-based virtual screening method. Further, the compounds were validated through absorption, distribution, metabolism and excretion filtering. Results: Based on binding energy, ADME properties, and toxicology analysis, we finally selected 3 compounds from structure-based virtual screening (ZINC ID: 75121653, 41131653, and 67266079) having binding energy -7.12, -7.1 and -7.08 Kcal/mol, respectively and 5 compounds from ligandbased virtual screening (ZINC ID: 05576502, 47654332, 04829153, 86434515 and 25626324) having binding energy -49.8, -54.9, -65.6, -61.1 and -66.7 Kcal/mol respectively. All these compounds have good ADME profile and reduced toxicity. Among eight compounds, one is soluble in water and remaining 7 compounds are highly soluble in water. All compounds have bioavailability 0.55 on the scale of 0 to 1. Among the 5 compounds from structure-based virtual screening, 2 compounds showed leadlikeness. All the compounds showed no inhibition of cytochrome P450 enzymes, no blood-brain barrier permeability and no toxic structure in medicinal chemistry profile. All the compounds are not a substrate of P-glycoprotein. Our predicted compounds may be capable of inhibiting 3C-like protease but need some further validation in wet lab.

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Rational design of inhibitors against LpxA protein of Acinetobacter baumannii using a virtual screening method

Journal of the Indian Chemical Society ◽

10.1016/j.jics.2021.100319 ◽

2021 ◽

pp. 100319

Author(s):

Sepideh Fereshteh ◽

Hourieh Kalhor ◽

Amin Sepehr ◽

Hamzeh Rahimi ◽

Mahdi Zafari ◽

...

Keyword(s):

Virtual Screening ◽

Acinetobacter Baumannii ◽

Rational Design ◽

Screening Method

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Generating Property-Matched Decoy Molecules Using Deep Learning

10.1101/2020.08.26.268193 ◽

2020 ◽

Author(s):

Fergus Imrie ◽

Anthony R. Bradley ◽

Charlotte M. Deane

Keyword(s):

Deep Learning ◽

Virtual Screening ◽

Method Development ◽

Screening Method ◽

Screening Methods ◽

Additional Risk ◽

Link Type ◽

Screening Performance ◽

And Training ◽

Virtual Screening Performance

An essential step in the development of virtual screening methods is the use of established sets of actives and decoys for benchmarking and training. However, the decoy molecules in commonly used sets are biased meaning that methods often exploit these biases to separate actives and decoys, rather than learning how to perform molecular recognition. This fundamental issue prevents generalisation and hinders virtual screening method development. We have developed a deep learning method (DeepCoy) that generates decoys to a user’s preferred specification in order to remove such biases or construct sets with a defined bias. We validated DeepCoy using two established benchmarks, DUD-E and DEKOIS 2.0. For all DUD-E targets and 80 of the 81 DEKOIS 2.0 targets, our generated decoy molecules more closely matched the active molecules’ physicochemical properties while introducing no discernible additional risk of false negatives. The DeepCoy decoys improved the Deviation from Optimal Embedding (DOE) score by an average of 81% and 66%, respectively, decreasing from 0.163 to 0.032 for DUD-E and from 0.109 to 0.038 for DEKOIS 2.0. Further, the generated decoys are harder to distinguish than the original decoy molecules via docking with Autodock Vina, with virtual screening performance falling from an AUC ROC of 0.71 to 0.63. The code is available at https://github.com/oxpig/DeepCoy. Generated molecules can be downloaded from http://opig.stats.ox.ac.uk/resources.

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Mulberrofuran G, a Potent Inhibitor of SPIKE Protein of SARS Corona Virus 2

Journal of Pharmaceutical Care ◽

10.18502/jpc.v9i2.6610 ◽

2021 ◽

Author(s):

Fatemeh Sadat Hosseini ◽

Mohammad Reza Motamedi

Keyword(s):

Molecular Docking ◽

Virtual Screening ◽

Active Site ◽

Potential Role ◽

Potent Inhibitor ◽

Screening Method ◽

Spike Protein ◽

Treatment Agent ◽

Approved Drugs ◽

Fda Approved Drugs

Background: At the onset of the 2020 year, Coronavirus disease (COVID-19) has become a pandemic and infected many people worldwide. Despite all efforts, no cure was found for this infection. Bioinformatics and medicinal chemistry have a potential role in the primary consideration of drugs to treat this infection. With virtual screening and molecular docking, some potent compounds and medications can be found and modified and then applied to treat disease in the next steps. Methods: By virtual screening method and PRYX software, some Food and Drug Administration (FDA) approved drugs and natural compounds have been docked with the SPIKE protein of SARS-CoV-2. Some more potent agents have been selected, and then new structures are designed with better affinity than them. After that, we searched for the molecules with a similar structure to designed compounds to find the most potent compound to our target. Results: Because of the study of structures and affinities, mulberrofuran G was the most potent compound in this study. The compound has interacted strongly with residues in the probably active site of SPIKE. Conclusion: Mulberrofuran G can be a treatment agent candidate for COVID-19 because of its good affinity to SPIKE of the virus and inhibition of virus-cell adhesion and entrance.

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Targeting FGL2, a molecular drug target for glioblastoma, with natural compounds through virtual screening method

Future Medicinal Chemistry ◽

10.4155/fmc-2020-0331 ◽

2021 ◽

Author(s):

Fahad Hassan Shah ◽

Song Ja Kim

Keyword(s):

Virtual Screening ◽

Active Site ◽

Drug Target ◽

Screening Method ◽

Tumor Development ◽

Pharmacokinetic Profile ◽

Natural Compounds ◽

Screening Platform ◽

Proliferation In Vitro

Background: Fibroleukin-2 protein (FGL2) causes redevelopment of brain tumors. Inhibition of these proteins has shown to improve glioblastoma prognosis and treatment efficacy. Aim: The current study gathered recently exploited natural compounds that suppress glioblastoma proliferation in vitro, tested against FGL2 protein. Method: Twenty-five compounds were explored through a virtual screening platform. Results: Three natural compounds (betanine, hesperetin and ovatodiolide) hit the active site of FGL2. Furthermore, the influence of these compounds was also assessed using in silico gene expression, and ADMET tools showed downregulation of some genes, which caused rapid tumor development while possessing a moderate acute toxicity and pharmacokinetic profile. Conclusion: Our study presents three compounds that are good candidates for evaluation in FGL2 mutated glioblastoma animal models.

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