Large-Scale Systematic Analysis of 2D Fingerprint Methods and Parameters to Improve Virtual Screening Enrichments

Sastry, G. Madhavi; Lowrie, Jeffrey F.; Dixon, Steven L.; Sherman, Woody

doi:10.1021/ci100062n

Cited by 301 publications

(248 citation statements)

References 41 publications

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“…The BCI and MDL fingerprints are dictionary-based, the Daylight and Pipeline Pilot fingerprints are molecule-based, and the Unity fingerprints employ both types of generation method. The use of these and other types of fingerprints for similaritybased virtual screening are described by Hert et al [12], Sastry et al [21] and Duan et al [22].…”

Section: Methodsmentioning

confidence: 99%

Effectiveness of 2D Fingerprints for Scaffold Hopping

et al. 2011

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Universities of LeedsMethods. This paper reports a detailed evaluation of the effectiveness of six common types of 2D fingerprint when they are used for scaffold hopping similarity searches of MDDR, WOMBAT and MUV data.Results. The results demonstrate that 2D fingerprints can be used for scaffold hopping, with novel scaffolds being identified in nearly every search that was carried out. The degree of enrichment depends on the structural diversity of the actives that are being sought, with the greatest enrichments often being obtained using ECFP4 fingerprints. Conclusions. 2D fingerprints provide a simple, and computationally efficient, way of identifying novel chemotypes in lead-discovery programmes.

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Section: Methodsmentioning

confidence: 99%

Effectiveness of 2D Fingerprints for Scaffold Hopping

et al. 2011

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“…Usually, database compounds are stored as line notations, such as SMILES, SMARTS and InChI, which are then converted into three-dimensional molecular structures. Converting the original files requires the correct assignment of stereochemistry, partial charges, and ionization states [174]. Table 5 provides some examples of well-established virtual databases used in virtual screening.…”

Section: Structure-based Virtual Screening (Sbvs)mentioning

confidence: 99%

Molecular Docking and Structure-Based Drug Design Strategies

et al. 2015

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Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure-and ligand-based methods.

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“…Molecular fingerprints are typically constructed by first representing a ligand as a 2D molecular graph, and then considering all possible bond paths within the molecule (11,12). The set of bond paths that characterize each molecule is unique, so that only identical molecules share exactly the same bond paths; similar molecules share most bond paths.…”

Section: Rmt Frameworkmentioning

confidence: 99%

“…Examples include a vector of measured or predicted physical properties (5-8), a vector enumerating the presence or absence of known functional groups on the ligand (9, 10), a vectorial representation of connectivities in the molecular graph (11,12) (known also as molecular fingerprints), and simply the 3D shape of the ligand (13)(14)(15)(16). Existing approaches then take the descriptor associated with each ligand and compare ligands with each other, for example through the Tanimoto coefficient (17,18).…”

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confidence: 99%

Predicting protein–ligand affinity with a random matrix framework

Lee

Brenner

Colwell

2016

Proc. Natl. Acad. Sci. U.S.A.

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Rapid determination of whether a candidate compound will bind to a particular target receptor remains a stumbling block in drug discovery. We use an approach inspired by random matrix theory to decompose the known ligand set of a target in terms of orthogonal "signals" of salient chemical features, and distinguish these from the much larger set of ligand chemical features that are not relevant for binding to that particular target receptor. After removing the noise caused by finite sampling, we show that the similarity of an unknown ligand to the remaining, cleaned chemical features is a robust predictor of ligand-target affinity, performing as well or better than any algorithm in the published literature. We interpret our algorithm as deriving a model for the binding energy between a target receptor and the set of known ligands, where the underlying binding energy model is related to the classic Ising model in statistical physics.drug discovery | random matrix theory | protein-ligand affinity | computational pharmacology | statistical physics F inding new ligands that bind to a given target is both a crucial step and a major stumbling block in modern drug discovery. Numerous attempts have been made to develop computational algorithms to predict the binding affinity of a ligand to a given receptor, which would allow potential compounds to be screened in silico, reducing costs and saving time. In particular, in response to the wealth of experimental data that exists both within pharmaceutical companies, and also in freely accessible online databases such as ChEMBL (1), approaches that attempt to "learn" from these data are increasingly gaining attention (2).An intuitive data-driven approach builds on the hypothesis that chemical commonalities among the known ligand set reveal salient features of the binding site. A corollary is that ligands with similar chemical functionality are expected to share similar binding affinity toward a particular receptor (3,4). This suggests that the known ligand set of a given target can be used to learn criteria that predict whether a novel ligand will bind to the target. This ligand-based approach is a powerful paradigm that does not require structural information about the receptor, which is potentially arduous to obtain, unlike other more atomistic methods such as docking or molecular dynamics.Any ligand-based method requires a way to quantify the chemical functionalities of a ligand, and various chemical descriptors have been proposed. Examples include a vector of measured or predicted physical properties (5-8), a vector enumerating the presence or absence of known functional groups on the ligand (9, 10), a vectorial representation of connectivities in the molecular graph (11, 12) (known also as molecular fingerprints), and simply the 3D shape of the ligand (13-16). Existing approaches then take the descriptor associated with each ligand and compare ligands with each other, for example through the Tanimoto coefficient (17, 18).Nonetheless, regardless of how ligand chemical func...

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Large-Scale Systematic Analysis of 2D Fingerprint Methods and Parameters to Improve Virtual Screening Enrichments

Cited by 301 publications

References 41 publications

Effectiveness of 2D Fingerprints for Scaffold Hopping

Effectiveness of 2D Fingerprints for Scaffold Hopping

Molecular Docking and Structure-Based Drug Design Strategies

Predicting protein–ligand affinity with a random matrix framework

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