Molecular Similarity in Medicinal Chemistry

Maggiora, Gerald M.; Vogt, Martin; Stumpfe, Dagmar; Bajorath, Jürgen

doi:10.1021/jm401411z

Cited by 559 publications

(529 citation statements)

References 169 publications

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“…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.…”

mentioning

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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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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“…Similarity searching is one of the most popular forms of ligand-based virtual screening [13][14][15][16] and has been one of our principal research areas for many years. Indeed, the widespread use of 2D fingerprints and the Tanimoto coefficient for computing molecular similarity is arguably due in large part to one of the first operational systems for similarity searching that was developed in a Sheffield collaboration with Pfizer in the mid-Eighties.…”

Section: Similarity-based Virtual Screeningmentioning

confidence: 99%

Chemoinformatics at the University of Sheffield 2002–2014

Gillet

Holliday

Willett

2015

Molecular Informatics

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“…Similarity-based approaches to virtual screening are very widely used in lead discovery programmes in the pharmaceutical and agrochemical industries 1,2 . In its simplest form, a known bioactive reference structure is matched against each of the structures in a chemical database to produce a ranking.…”

Section: Introductionmentioning

confidence: 99%

Maximum Common Substructure-Based Data Fusion in Similarity Searching

Duesbury

Holliday

Willett

2015

J. Chem. Inf. Model.

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Data fusion has been shown to work very well when applied to fingerprint-based similarity searching, yet little is known of its application to Maximum Common Substructure (MCS)-based similarity searching.Two similarity search applications of the MCS will be focussed on here. Typically, the number of bonds in the MCS, as well as the bonds in the two molecules being compared, are used in a similarity coefficient. The power of this technique can be extended using data fusion, where the MCS similarities of a set of reference molecules against one database molecule are fused. This "group fusion" technique forms the first application of the MCS in this work. The other application is that of the chemical hyperstructure. The hyperstructure concept is an alternative form of data fusion, being a hypothetical molecule that is constructed from the overlap of a set of existing molecules. This paper compares fingerprint group fusion (extended-connectivity fingerprints), MCS similarity group fusion, and hyperstructure similarity searching, and describes their relative merits and complementarity in virtual screening. It is concluded that * To whom correspondence should be addressed 1 the hyperstructure approach as implemented here is less generally effective than conventional fingerprint approaches.

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Molecular Similarity in Medicinal Chemistry

Cited by 559 publications

References 169 publications

Predicting protein–ligand affinity with a random matrix framework

Predicting protein–ligand affinity with a random matrix framework

Chemoinformatics at the University of Sheffield 2002–2014

Maximum Common Substructure-Based Data Fusion in Similarity Searching

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