Comparison of Shape-Matching and Docking as Virtual Screening Tools

2007 ◽  
Vol 50 (1) ◽  
pp. 74-82 ◽  
Author(s):  
Paul C. D. Hawkins ◽  
A. Geoffrey Skillman ◽  
Anthony Nicholls
Keyword(s):  
Virtual Screening ◽  
Shape Matching ◽  
Screening Tools

scholarly journals Comprehensive Comparison of Ligand-Based Virtual Screening Tools Against the DUD Data set Reveals Limitations of Current 3D Methods

2010 ◽  
Vol 50 (12) ◽  
pp. 2079-2093 ◽  
Author(s):  
Vishwesh Venkatraman ◽  
Violeta I. Pérez-Nueno ◽  
Lazaros Mavridis ◽  
David W. Ritchie
Keyword(s):  
Virtual Screening ◽  
Screening Tools ◽  
Data Set ◽  
Comprehensive Comparison ◽  
3D Methods

Comparison of Ligand-Based and Receptor-Based Virtual Screening of HIV Entry Inhibitors for the CXCR4 and CCR5 Receptors Using 3D Ligand Shape Matching and Ligand−Receptor Docking

2008 ◽  
Vol 48 (3) ◽  
pp. 509-533 ◽  
Author(s):  
Violeta I. Pérez-Nueno ◽  
David W. Ritchie ◽  
Obdulia Rabal ◽  
Rosalia Pascual ◽  
Jose I. Borrell ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Shape Matching ◽  
Entry Inhibitors ◽  
Hiv Entry ◽  
Hiv Entry Inhibitors

ChemInform Abstract: Optimization of High Throughput Virtual Screening by Combining Shape-Matching and Docking Methods.

ChemInform ◽  
2008 ◽  
Vol 39 (24) ◽  
Author(s):  
Hui Sun Lee ◽  
Jiwon Choi ◽  
Irina Kufareva ◽  
Ruben Abagyan ◽  
Anton Filikov ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
Shape Matching ◽  
Abstract Optimization ◽  
High Throughput Virtual Screening

Comparison of Automated Docking Programs as Virtual Screening Tools

2005 ◽  
Vol 48 (4) ◽  
pp. 962-976 ◽  
Author(s):  
Maxwell D. Cummings ◽  
Renee L. DesJarlais ◽  
Alan C. Gibbs ◽  
Venkatraman Mohan ◽  
Edward P. Jaeger
Keyword(s):  
Virtual Screening ◽  
Screening Tools ◽  
Automated Docking

scholarly journals Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification

2012 ◽  
Vol 9 (77) ◽  
pp. 3196-3207 ◽  
Author(s):  
Pedro J. Ballester ◽  
Martina Mangold ◽  
Nigel I. Howard ◽  
Richard L. Marchese Robinson ◽  
Chris Abell ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput Screening ◽  
Streptomyces Coelicolor ◽  
Screening Tools ◽  
Molecular Scaffolds ◽  
Efficient Manner ◽  
Modern Drug ◽  
Screening Protocol ◽  
Molecular Databases ◽  
Hit Identification

One of the initial steps of modern drug discovery is the identification of small organic molecules able to inhibit a target macromolecule of therapeutic interest. A small proportion of these hits are further developed into lead compounds, which in turn may ultimately lead to a marketed drug. A commonly used screening protocol used for this task is high-throughput screening (HTS). However, the performance of HTS against antibacterial targets has generally been unsatisfactory, with high costs and low rates of hit identification. Here, we present a novel computational methodology that is able to identify a high proportion of structurally diverse inhibitors by searching unusually large molecular databases in a time-, cost- and resource-efficient manner. This virtual screening methodology was tested prospectively on two versions of an antibacterial target (type II dehydroquinase from Mycobacterium tuberculosis and Streptomyces coelicolor ), for which HTS has not provided satisfactory results and consequently practically all known inhibitors are derivatives of the same core scaffold. Overall, our protocols identified 100 new inhibitors, with calculated K i ranging from 4 to 250 μM (confirmed hit rates are 60% and 62% against each version of the target). Most importantly, over 50 new active molecular scaffolds were discovered that underscore the benefits that a wide application of prospectively validated in silico screening tools is likely to bring to antibacterial hit identification.

scholarly journals Virtual Screening for Potential Phytobioactives as Therapeutic Leads to Inhibit NQO1 for Selective Anticancer Therapy

Molecules ◽  
2021 ◽  
Vol 26 (22) ◽  
pp. 6863
Author(s):  
Bhargav Shreevatsa ◽  
Chandan Dharmashekara ◽  
Vikas Halasumane Swamy ◽  
Meghana V. Gowda ◽  
Raghu Ram Achar ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Binding Site ◽  
Anticancer Agents ◽  
Substrate Binding ◽  
Tumor Burden ◽  
Screening Tools ◽  
Tumor Promoter ◽  
Substrate Binding Site

NAD(P)H:quinone acceptor oxidoreductase-1 (NQO1) is a ubiquitous flavin adenine dinucleotide-dependent flavoprotein that promotes obligatory two-electron reductions of quinones, quinonimines, nitroaromatics, and azo dyes. NQO1 is a multifunctional antioxidant enzyme whose expression and deletion are linked to reduced and increased oxidative stress susceptibilities. NQO1 acts as both a tumor suppressor and tumor promoter; thus, the inhibition of NQO1 results in less tumor burden. In addition, the high expression of NQO1 is associated with a shorter survival time of cancer patients. Inhibiting NQO1 also enables certain anticancer agents to evade the detoxification process. In this study, a series of phytobioactives were screened based on their chemical classes such as coumarins, flavonoids, and triterpenoids for their action on NQO1. The in silico evaluations were conducted using PyRx virtual screening tools, where the flavone compound, Orientin showed a better binding affinity score of −8.18 when compared with standard inhibitor Dicumarol with favorable ADME properties. An MD simulation study found that the Orientin binding to NQO1 away from the substrate-binding site induces a potential conformational change in the substrate-binding site, thereby inhibiting substrate accessibility towards the FAD-binding domain. Furthermore, with this computational approach we are offering a scope for validation of the new therapeutic components for their in vitro and in vivo efficacy against NQO1.

Positive Predictive Value Surfaces as a Complementary Tool to Assess the Performance of Virtual Screening Methods

2020 ◽  
Vol 20 (14) ◽  
pp. 1447-1460
Author(s):  
Juan F. Morales ◽  
Sara Chuguransky ◽  
Lucas N. Alberca ◽  
Juan I. Alice ◽  
Sofía Goicoechea ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Positive Predictive Value ◽  
Ensemble Learning ◽  
Predictive Value ◽  
Experimental Testing ◽  
Screening Tools ◽  
Screening Methods ◽  
Learning Approaches ◽  
Classification Problems ◽  
Screening Experiments

Background: Since their introduction in the virtual screening field, Receiver Operating Characteristic (ROC) curve-derived metrics have been widely used for benchmarking of computational methods and algorithms intended for virtual screening applications. Whereas in classification problems, the ratio between sensitivity and specificity for a given score value is very informative, a practical concern in virtual screening campaigns is to predict the actual probability that a predicted hit will prove truly active when submitted to experimental testing (in other words, the Positive Predictive Value - PPV). Estimation of such probability is however, obstructed due to its dependency on the yield of actives of the screened library, which cannot be known a priori. Objective: To explore the use of PPV surfaces derived from simulated ranking experiments (retrospective virtual screening) as a complementary tool to ROC curves, for both benchmarking and optimization of score cutoff values. Methods: The utility of the proposed approach is assessed in retrospective virtual screening experiments with four datasets used to infer QSAR classifiers: inhibitors of Trypanosoma cruzi trypanothione synthetase; inhibitors of Trypanosoma brucei N-myristoyltransferase; inhibitors of GABA transaminase and anticonvulsant activity in the 6 Hz seizure model. Results: Besides illustrating the utility of PPV surfaces to compare the performance of machine learning models for virtual screening applications and to select an adequate score threshold, our results also suggest that ensemble learning provides models with better predictivity and more robust behavior. Conclusion: PPV surfaces are valuable tools to assess virtual screening tools and choose score thresholds to be applied in prospective in silico screens. Ensemble learning approaches seem to consistently lead to improved predictivity and robustness.

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