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About 10,000 papers for “virtual screening”

Virtual screening

Ozge Yoluk, Susanne Sauer, Alisa Solovyeva et al. · Elsevier eBooks · 2026

Cited by 0

Virtual Screening

N. Yellasubbaiah, B. Sivakumar, B. Nagasudha · 2024

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. Although the cause and pathophysiology of the disease are unknown, acetylcholinesterase (AChE) has been identified as a potential therapeutic target. Compounds from the Drug Bank and ZINC subset drug-like databases were screened using ligand-based pharmacophore filtering and molecular docking in this study. Other potential therapeutic targets under investigation include glycogen synthase kinase 3 (GSK-3), beta-secretase (BACE-1), and others. The combined analysis identified 139 potentially active and multi-target ligands targeting two or more enzymes, including c-Jun N-terminal kinase 3 (JNK-3), protein tyrosine phosphatase 1B (PTP1B), and nicotinamide adenine dinucleotide phosphate oxidase. ZINC-27 and ZINC-1–4, which inhibit AChE, GSK-3, and BACE-1, were discovered utilizing a combination of compound profiling, ADMET prediction, and structural modification. In conclusion, the multi-approach study revealed that ZINC-27 and ZINC-1–4 have the potential to be multi-target anti-AD drugs that simultaneously target AChE, GSK-3, and BACE-1, and thus warrant further investigation.

Cited by 0

Virtual screening.

Lorraine Marsh (210221) · Figshare · 2015

<p>Results of ROC-AUC analysis for 39 DUD protein virtual screening analyses are shown. AUC values obtained by the internal consensus method are compared to those from Vina scoring. Values above 0.5 indicate successful selection of ligands over decoys. The differences between Vina and the internal consensus method are significant (two-tailed, paired T-test; p<2.0×10<sup>−7</sup>).</p>

Cited by 0Open access

Virtual Screening

Khaled Barakat, Jonathan Y. Mane, Jack A. Tuszyński · IGI Global eBooks · 2011

Virtual screening, or VS, is emerging as a valuable tool in discovering new candidate inhibitors for many biologically relevant targets including the many chemotherapeutic targets that play key roles in cell signaling pathways. However, despite the great advances made in the field thus far, VS is still in constant development with a relatively low success rate that needs to be improved by parallel experimental validation methods. This chapter reviews the recent advances in VS, focusing on the range and type of computational methods and their successful applications in drug discovery. The chapter also discusses both the advantages and limitations of the various techniques used in VS and outlines a number of future directions in which the field may progress.

Cited by 7

Virtual Screening

Karl‐Heinz Baringhaus, Gerhard Heßler · 2015

This chapter focuses on requirements and aspects of virtual screening, which aims to explore large chemical databases at minimal costs. The used rationale either is the known protein structure of the target (structure-based virtual screening) or employs information from known actives (ligand-based virtual screening). Depending on the actual knowledge and goal of the virtual screening, methods and databases have to be carefully chosen. Successful virtual screening applications require the definition and the careful validation of the virtual screening strategy. Important aspects of the virtual screening strategy comprise questions like which virtual screening methods are used, whether these methods are applied in parallel or used sequentially in a cascaded virtual screening, and how many compounds can be selected for biological testing. The concepts developed for virtual screening find application in related scientific areas, for example, in chemogenomics or in in silico profiling applications.

Cited by 2

Virtual Screening

Alan Talevi, Eduardo A. Castro, Luis E. Bruno‐Blanch · IGI Global eBooks · 2011

In this chapter, the authors analyze virtual screening advantages and the classification of virtual screening approaches. They also discuss the current and potential importance of virtual screening for drug development in Latin America. Finally, they present a brief overview on virtual screening perspectives.

Cited by 4

Virtual screening

John H. Van Drie · ˜The œbiomedical & life sciences collection. · 2021

Cited by 0

Virtual Screening

Christoph Sotriffer · Methods and principles in medicinal chemistry · 2011

Cited by 57

Virtual screening.

Chiara Bianca Maria Platania (138843), Salvatore Salomone (138847), Gian Marco Leggio (138849) et al. · Figshare · 2015

<p>Pose of pramipexole (cyan lines) and compound ZINC45254546 (magenta lines, see also text) docked into hD<sub>3</sub> (A) and hD<sub>2L</sub> (B) optimized receptor structures. H-bonds with Aspartate conserved residues are represented with yellow dashes.</p>

Cited by 0Open access

Virtual Screening

Angie Atoche Puelles, Luana Luiza Bastos, Vivian Morais Paixão et al. · Advances in bioinformatics and biomedical engineering book series · 2024

Advancements in computational methods have significantly transformed the traditional drug discovery process, which has been historically lengthy and costly, often taking over a decade and billions of dollars. Virtual screening (VS) is a computational technique used in drug discovery to identify potential drug candidates by screening large chemical libraries. This technique has transformed drug discovery by enabling rapid and cost-effective screening of chemical libraries, reducing time and resources. This leverages algorithms, such as similarity-based, quantitative, and machine learning, to prioritize compounds binding to target proteins. Additionally, databases provide vast compound collections for screening. However, experimental validation is essential to ensure accuracy. In this chapter, the authors delve into the concepts and methodologies of this technique, focusing on its crucial role in drug discovery.

Cited by 0

virtual screening

2024

Citation: 'virtual screening' in the IUPAC Compendium of Chemical Terminology, 5th ed.; International Union of Pure and Applied Chemistry; 2025. Online version 5.0.0, 2025. 10.1351/goldbook.11533 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for individual terms. Requests for commercial usage of the compendium should be directed to IUPAC.

Cited by 0

Virtual screening workflow.

Chiara Ruggeri (800455), Nyssa Drinkwater (694048), Komagal Kannan Sivaraman (800456) et al. · Figshare · 2015

Cited by 0Open access

Fuzzy virtual ligands for virtual screening

Martin Löwer, Yusuf Tanrıkulu, Martin Weisel et al. · Chemistry Central Journal · 2009

A new method to bridge the gap between ligand and receptor-based methods in virtual screening (VS) is presented. We introduce a structure-derived virtual ligand (VL) model as an extension to a previously published pseudo-ligand technique [1]: LIQUID [2] pharmacophore virtual screening is combined with grid-based protein binding site predictions of PocketPicker [3]. This approach might help reduce bias introduced by manual selection of binding site residues and introduces pocket shape information to the VL. It allows for a combination of several protein structure models into a single fuzzy VL representation, which can be used to scan screening compound collections for ligand structures with a similar potential pharmacophore. PocketPicker employs an elaborate grid-based scanning procedure to determine buried cavities and depressions on the protein's surface. Potential binding sites are represented by clusters of grid probes characterizing the shape and accessibility of a cavity. A rule-based system is then applied to project reverse pharmacophore types onto the grid probes of a selected pocket. The pocket pharmacophore types are assigned depending on the properties and geometry of the protein residues surrounding the pocket with regard to their relative position towards the grid probes. LIQUID is used to cluster representative pocket probes by their pharmacophore types describing a VL model. The VL is encoded in a correlation vector, which can then be compared to a database of pre-calculated ligand models. A retrospective screening using the VL and several protein structures was evaluated by ten fold cross-validation with ROC-AUC and BEDROC metrics, obtaining a significant enrichment of actives. Future work will be devoted to prospective screening using a novel protein target of Helicobacter pylori and compounds from commercial providers.

Cited by 0Open access