FieldScreen: Virtual Screening Using Molecular Fields. Application to the DUD Data Set

Cheeseright, Timothy J.; Mackey, Mark; Melville, James L.; Vinter, Jeremy G.

doi:10.1021/ci800110p

Cited by 115 publications

(165 citation statements)

References 44 publications

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“…Following the approach of Cheeseright et al [26], we generated a reduced version of DUD for 13 of the targets, with chemotype information added, using the ZINC codes included in their supplementary information. We used the DUD sets with MMFF94x charges for this purpose.…”

Section: Chemotypesmentioning

confidence: 99%

“…To demonstrate that ElectroShape is not simply a sophisticated substructure search method only capable of finding actives that share a common chemotype with the query ligand, further validation was carried out by taking into account chemotype redundancy in the DUD active sets as described by Cheeseright et al [26]. The results demonstrate that ElectroShape has good cross-chemotype enrichments.…”

Section: Introductionmentioning

confidence: 99%

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ElectroShape: fast molecular similarity calculations incorporating shape, chirality and electrostatics

Armstrong¹,

Morris²,

Finn³

et al. 2010

J Comput Aided Mol Des

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We present ElectroShape, a novel ligand-based virtual screening method, that combines shape and electrostatic information into a single, unified framework. Building on the ultra-fast shape recognition (USR) approach for fast non-superpositional shape-based virtual screening, it extends the method by representing partial charge information as a fourth dimension. It also incorporates the chiral shape recognition (CSR) method, which distinguishes enantiomers. It has been validated using release 2 of the Directory of useful decoys (DUD), and shows a near doubling in enrichment ratio at 1% over USR and CSR, and improvements as measured by Receiver Operating Characteristic curves. These improvements persisted even after taking into account the chemotype redundancy in the sets of active ligands in DUD. During the course of its development, ElectroShape revealed a difference in the charge allocation of the DUD ligand and decoy sets, leading to several new versions of DUD being generated as a result. ElectroShape provides a significant addition to the family of ultra-fast ligand-based virtual screening methods, and its higher-dimensional shape recognition approach has great potential for extension and generalisation.

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Section: Chemotypesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ElectroShape: fast molecular similarity calculations incorporating shape, chirality and electrostatics

Armstrong¹,

Morris²,

Finn³

et al. 2010

J Comput Aided Mol Des

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show abstract

“…BABEL is based on the 2D-fingerprints of ligands. 17 Also compared are the popular structure-based methods Glide, 18−20 DOCK, 21 and GOLD. 22 Comparing the mean AUC values in Table 3, we observe that VS-APPLE performs comparably to the structure-based method Glide and significantly better than the other structure-based methods (DOCK and GOLD) and the ligand-based methods (ROCS and BABEL).…”

Section: ■ Results and Discussionmentioning

confidence: 99%

VS-APPLE: A Virtual Screening Algorithm Using Promiscuous Protein–Ligand Complexes

Okuno

Kato

Terada

et al. 2015

J. Chem. Inf. Model.

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As the number of structurally resolved protein-ligand complexes increases, the ligand-binding pockets of many proteins have been found to accommodate multiple different compounds. Effective use of these structural data is important for developing virtual screening (VS) methods that identify bioactive compounds. Here, we introduce a VS method, VS-APPLE (Virtual Screening Algorithm using Promiscuous Protein-Ligand complExes), based on promiscuous protein-ligand binding structures. In VS-APPLE, multiple ligands bound to a pocket are combined into a query template for screening. Both the structural match between a test compound and the multiple-ligand template and the possible collisions between the test compound and the target protein are evaluated by an efficient geometric hashing method. The performance of VS-APPLE was examined on a filtered, clustered version of the Directory of Useful Decoys data set. In Area Under the Curve analyses of this data set, VS-APPLE outperformed several popular screening programs. Judging from the performance of VS-APPLE, the structural data of promiscuous protein-ligand bindings could be further analyzed and exploited for developing VS methods.

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“…The reason behind the choice of this particular software is simply the fact that it is commercially available: the calculations have been run on the generic database that is provided in the software package making this study not dependent on in-house scripts and/or in-house collection of chemical fragments. SparkV10 (also known as the previous name of FieldStere) is a computational methodology developed and distributed by the software company Cresset [69]. SparkV10 uses Cresset's field technology to find biologically equivalent replacements for a selected moiety in the molecule under investigation, enabling the user to find new structures in new chemical space.…”

Section: Bioisosteric Replacements In Nk 1 Receptor Antagonist: a Retmentioning

confidence: 99%

Case Study 2: Bioisosteric Replacements for the Neurokinin 1 Receptor (NK1R)

Perruccio

2013

Methods and Principles in Medicinal Chemistry

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FieldScreen: Virtual Screening Using Molecular Fields. Application to the DUD Data Set

Cited by 115 publications

References 44 publications

ElectroShape: fast molecular similarity calculations incorporating shape, chirality and electrostatics

ElectroShape: fast molecular similarity calculations incorporating shape, chirality and electrostatics

VS-APPLE: A Virtual Screening Algorithm Using Promiscuous Protein–Ligand Complexes

Case Study 2: Bioisosteric Replacements for the Neurokinin 1 Receptor (NK1R)

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