Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?

Bajusz, Dávid; Rácz, Anita; Héberger, Károly

doi:10.1186/s13321-015-0069-3

Cited by 926 publications

(834 citation statements)

References 35 publications

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“…This observation suggested that Ͼ90% of the primary screen hits were either of low potency or impacted the VSV pseudotype and its luciferase reporter rather than MARV GP function. Chemical fingerprinting to determine the structural relationship of the 554 compounds was done using the Tanimoto criteria (36), and the results are graphically represented in Fig. 1A.…”

Section: Identification Of 7200 Compounds As Inhibitors Of Infectionmentioning

confidence: 99%

Large-Scale Screening and Identification of Novel Ebola Virus and Marburg Virus Entry Inhibitors

Anantpadma

Kouznetsova

Wang

et al. 2016

Antimicrob Agents Chemother

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Section: Identification Of 7200 Compounds As Inhibitors Of Infectionmentioning

confidence: 99%

Large-Scale Screening and Identification of Novel Ebola Virus and Marburg Virus Entry Inhibitors

Anantpadma

Kouznetsova

Wang

et al. 2016

Antimicrob Agents Chemother

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“…The Tanimoto index is one of the most common metrics for fingerprint-based molecular similarity calculations and has recently been shown to be among the best choices for this purpose (Bajusz et al, 2015). For the comparison of molecular similarity, three Tanimoto coefficients were computed: the maximum Tanimoto coefficient to actives in the training set (T 1 ), the average Tanimoto coefficient to actives in the training set (T 2 ), and the maximum Tanimoto coefficient to all inactives in the training set (T 3 ).…”

Section: Molecular Similaritymentioning

confidence: 99%

Molecular similarity-based predictions of the Tox21 screening outcome

Drwal

Siramshetty

Banerjee

et al. 2015

Front. Environ. Sci.

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Citation:Drwal MN, Siramshetty VB, Banerjee P, Goede A, Preissner R and Dunkel M (2015) To assess the toxicity of new chemicals and drugs, regulatory agencies require in vivo testing for many toxic endpoints, resulting in millions of animal experiments conducted each year. However, following the Replace, Reduce, Refine (3R) principle, the development and optimization of alternative methods, in particular in silico methods, has been put into focus in the recent years. It is generally acknowledged that the more complex a toxic endpoint, the more difficult it is to model. Therefore, computational toxicology is shifting from modeling general and complex endpoints to the investigation and modeling of pathways of toxicity and the underlying molecular effects. The U.S. Toxicology in the twenty-first century (Tox21) initiative has screened a large library of compounds, including approximately 10K environmental chemicals and drugs, for different mechanisms responsible for eliciting toxic effects, and made the results publicly available. Through the Tox21 Data Challenge, the consortium has established a platform for computational toxicologists to develop and validate their predictive models. Here, we present a fast and successful method for the prediction of different outcomes of the nuclear receptor and stress response pathway screening from the Tox21 Data Challenge 2014. The method is based on the combination of molecular similarity calculations and a naïve Bayes machine learning algorithm and has been implemented as a KNIME pipeline. Molecules are represented as binary vectors consisting of a concatenation of common two-dimensional molecular fingerprint types with topological compound properties. The prediction method has been optimized individually for each modeled target and evaluated in a cross-validation as well as with the independent Tox21 validation set. Our results show that the method can achieve good prediction accuracies and rank among the top algorithms submitted to the prediction challenge, indicating its broad applicability in toxicity prediction.

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“…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).…”

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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Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?

Cited by 926 publications

References 35 publications

Large-Scale Screening and Identification of Novel Ebola Virus and Marburg Virus Entry Inhibitors

Large-Scale Screening and Identification of Novel Ebola Virus and Marburg Virus Entry Inhibitors

Molecular similarity-based predictions of the Tox21 screening outcome

Predicting protein–ligand affinity with a random matrix framework

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