Rapid Shape-Based Ligand Alignment and Virtual Screening Method Based on Atom/Feature-Pair Similarities and Volume Overlap Scoring

2011 ◽  
Vol 51 (10) ◽  
pp. 2455-2466 ◽  
Author(s):  
G. Madhavi Sastry ◽  
Steven L. Dixon ◽  
Woody Sherman
Keyword(s):  
Virtual Screening ◽  
Screening Method ◽  
Feature Pair ◽  
Ligand Alignment

Lead Molecule Prediction and Characterization for Designing MERS-CoV 3C-like Protease Inhibitors: An In silico Approach

2018 ◽  
Vol 15 (1) ◽  
pp. 82-88 ◽  
Author(s):  
Md. Mostafijur Rahman ◽  
Md. Bayejid Hosen ◽  
M. Zakir Hossain Howlader ◽  
Yearul Kabir
Keyword(s):  
Binding Energy ◽  
Middle East ◽  
Virtual Screening ◽  
In Silico ◽  
Screening Method ◽  
Middle East Respiratory Syndrome ◽  
Cytochrome P450 Enzymes ◽  
Drug Molecules ◽  
Essential Enzyme ◽  
Reduced Toxicity

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.

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

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

scholarly journals Generating Property-Matched Decoy Molecules Using Deep Learning

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.

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

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.

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

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.

scholarly journals Study on Structure Activity Relationship of Natural Flavonoids against Thrombin by Molecular Docking Virtual Screening Combined with Activity Evaluation In Vitro

Molecules ◽  
2020 ◽  
Vol 25 (2) ◽  
pp. 422
Author(s):  
Xiaoyan Wang ◽  
Zhen Yang ◽  
Feifei Su ◽  
Jin Li ◽  
Evans Owusu Boadi ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Screening Method ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Oh Groups ◽  
Structure Activity Relationships ◽  
Thrombin Inhibition ◽  
Structure Activity

Thrombin, a key enzyme of the serine protease superfamily, plays an integral role in the blood coagulation cascade and thrombotic diseases. In view of this, it is worthwhile to establish a method to screen thrombin inhibitors (such as natural flavonoid-type inhibitors) as well as investigate their structure activity relationships. Virtual screening using molecular docking technique was used to screen 103 flavonoids. Out of this number, 42 target compounds were selected, and their inhibitory effects on thrombin assayed by chromogenic substrate method. The results indicated that the carbon-carbon double bond group at the C2, C3 sites and the carbonyl group at the C4 sites of flavones were essential for thrombin inhibition, whereas the methoxy and O-glycosyl groups reduced thrombin inhibition. Noteworthy, introduction of OH groups at different positions on flavonoids either decreased or increased anti-thrombin potential. Myricetin exhibited the highest inhibitory potential against thrombin with an IC50 value of 56 μM. Purposively, the established molecular docking virtual screening method is not limited to exploring flavonoid structure activity relationships to anti-thrombin activity but also usefully discovering other natural active constituents.

scholarly journals Improved method of structure-based virtual screening based on ensemble learning

RSC Advances ◽  
2020 ◽  
Vol 10 (13) ◽  
pp. 7609-7618
Author(s):  
Jin Li ◽  
WeiChao Liu ◽  
Yongping Song ◽  
JiYi Xia
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
Ensemble Learning ◽  
High Throughput ◽  
High Throughput Screening ◽  
Screening Method ◽  
Improved Method ◽  
Complementary Technique

Virtual screening has become a successful alternative and complementary technique to experimental high-throughput screening technologies for drug design. This paper proposed a target-specific virtual screening method based on ensemble learning named ENS-VS.

scholarly journals Identification of novel CDK2 inhibitors by a multistage virtual screening method based on SVM, pharmacophore and docking model

2019 ◽  
Vol 35 (1) ◽  
pp. 235-244 ◽  
Author(s):  
Jing-Wei Liang ◽  
Ming-Yang Wang ◽  
Shan Wang ◽  
Shi-Long Li ◽  
Wan-Qiu Li ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Screening Method ◽  
Docking Model

scholarly journals Virtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids

2020 ◽  
Author(s):  
Chaitanya K. Jaladanki ◽  
Yang He ◽  
Li Na Zhao ◽  
Sebastian Maurer-Stroh ◽  
Lit-Hsin Loo ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Virtual Screening ◽  
Nuclear Receptors ◽  
Endocrine Disrupting Chemicals ◽  
Screening Method ◽  
The Body ◽  
Cyclopropane Fatty Acid ◽  
Peroxisome Proliferator ◽  
Endocrine Disrupting ◽  
Multiple Structures

Abstract Nuclear receptors (NRs) are key regulators of energy homeostasis, body development, and sexual reproduction. Xenobiotics binding to NRs may disrupt natural hormonal systems and induce undesired adverse effects in the body. However, many chemicals of concerns have limited or no experimental data on their potential or lack-of-potential endocrine-disrupting effects. Here, we propose a virtual screening method based on molecular docking for predicting potential endocrine-disrupting chemicals (EDCs) that bind to NRs. For 12 NRs, we systematically analyzed how multiple crystal structures can be used to distinguish actives and inactives found in previous high-throughput experiments. Our method is based on (i) consensus docking scores from multiple structures at a single functional state (agonist-bound or antagonist-bound), (ii) multiple functional states (agonist-bound and antagonist-bound), and (iii) multiple pockets (orthosteric site and alternative sites) of these NRs. We found that the consensus enrichment from multiple structures is better than or comparable to the best enrichment from a single structure. The discriminating power of this consensus strategy was further enhanced by a chemical similarity-weighted scoring scheme, yielding better or comparable enrichment for all studied NRs. Applying this optimized method, we screened 252 fatty acids against peroxisome proliferator-activated receptor gamma (PPARγ) and successfully identified 3 previously unknown fatty acids with Kd = 100–250 μM including two furan fatty acids: furannonanoic acid (FNA) and furanundecanoic acid (FUA), and one cyclopropane fatty acid: phytomonic acid (PTA). These results suggested that the proposed method can be used to rapidly screen and prioritize potential EDCs for further experimental evaluations.

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