Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures

Zhang, Bijun; Vogt, Martin; Maggiora, Gerald M.; Bajorath, Jürgen

doi:10.1007/s10822-015-9872-1

Cited by 50 publications

(65 citation statements)

References 31 publications

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“…In particular, we used the RDKit [73] (http://rdkit.org/) MCS nodes for the MCS calculations. To provide a metric for the MCS, we followed the recent analyses of Bajorath and colleagues [65, 74, 75]. Thus they recognised that a similarity equivalent to the Tanimoto similarity for a molecule A with a total of |A| b heavy atoms and another molecule B with |B| b heavy atoms, could be written in the form [74]where |MCS(A, B)| b is the number of heavy atoms in the MCS.…”

Section: Methodsmentioning

confidence: 99%

Analysis of drug–endogenous human metabolite similarities in terms of their maximum common substructures

O’Hagan

Kell

2017

J Cheminform

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In previous work, we have assessed the structural similarities between marketed drugs (‘drugs’) and endogenous natural human metabolites (‘metabolites’ or ‘endogenites’), using ‘fingerprint’ methods in common use, and the Tanimoto and Tversky similarity metrics, finding that the fingerprint encoding used had a dramatic effect on the apparent similarities observed. By contrast, the maximal common substructure (MCS), when the means of determining it is fixed, is a means of determining similarities that is largely independent of the fingerprints, and also has a clear chemical meaning. We here explored the utility of the MCS and metrics derived therefrom. In many cases, a shared scaffold helps cluster drugs and endogenites, and gives insight into enzymes (in particular transporters) that they both share. Tanimoto and Tversky similarities based on the MCS tend to be smaller than those based on the MACCS fingerprint-type encoding, though the converse is also true for a significant fraction of the comparisons. While no single molecular descriptor can account for these differences, a machine learning-based analysis of the nature of the differences (MACCS_Tanimoto vs MCS_Tversky) shows that they are indeed deterministic, although the features that are used in the model to account for this vary greatly with each individual drug. The extent of its utility and interpretability vary with the drug of interest, implying that while MCS is neither ‘better’ nor ‘worse’ for every drug–endogenite comparison, it is sufficiently different to be of value. The overall conclusion is thus that the use of the MCS provides an additional and valuable strategy for understanding the structural basis for similarities between synthetic, marketed drugs and natural intermediary metabolites. Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0198-y) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Analysis of drug–endogenous human metabolite similarities in terms of their maximum common substructures

O’Hagan

Kell

2017

J Cheminform

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“…This study seeks to address underestimated overall flow regime alteration in the traditional RVA method through the development of a revised RVA method. In this revised method, the positional information of each IHA is measured using a first-order connectivity index (FOCI; lateral alteration) [32,33]; the effect of the ICOHY on flow regime alteration is assessed through the Tanimoto alteration (TA; longitudinal alteration), which is calculated using the Tanimoto similarity (TS) method [34,35]. The TS method, which can be used to calculate the similarity of data series, has widely been used in Deoxyribonucleic acid (DNA) engineering.…”

Section: Methodsmentioning

confidence: 99%

Quantitative Assessment of Flow Regime Alteration Using a Revised Range of Variability Methods

Peng

Huang

et al. 2018

Water

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Abstract:The Ecological Limits of Hydrologic Alteration (ELOHA) framework, which can be used to determine and implement environmental flows at regional scales, requires accurate flow regime alteration assessment. The widely used range of variability approach (RVA) evaluates flow regime alteration by comparing the distribution of 32 Indicators of Hydrologic Alteration (IHA). However, the traditional RVA method is not comprehensive, because it neglects both the human-induced inner characteristics of one hydrological year (ICOHY) and the positional information of 32 IHA, which are the main factors behind ecosystem alteration. To address these limitations, we propose a revised RVA method that uses the Tanimoto similarity (TS) coefficient to reflect the ICOHY and a first-order connectivity index to reflect the IHA positional information. The yearly Tanimoto alteration (TA) index is measured using the revised RVA method, and the individual alteration (IA) values of each of 32 IHA are calculated using the traditional RVA method. Then, a new index, the overall degree of flow regime alteration (OA), is derived from the TA and IA values. The effectiveness of the revised RVA method is tested in the upper reaches of the Yangtze River, and the results suggest that the revised RVA ameliorates the limitations of the traditional RVA, and therefore, is preferable for use in the ELOHA framework.

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“…The consensus scores were calculated and ranked. The top 2% of molecules with high consensus ranking scores were retained and clustered to 10 sets based on their similarity using Tanimoto coefficient value (fingerprinter FCFP_6) (Godden, Xue & Bajorath, 2000; Bajusz, Racz & Heberger, 2015; Zhang et al, 2015) in Discovery Studio 4.0. Finally, compounds with the highest consensus scores were picked out from each set and subjected to the bioassay.…”

Section: Methodsmentioning

confidence: 99%

Discovery of novel inhibitors disrupting HIF-1α/von Hippel–Lindau interaction through shape-based screening and cascade docking

Xue

Zhao²,

et al. 2016

PeerJ

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Major research efforts have been devoted to the discovery and development of new chemical entities that could inhibit the protein–protein interaction between HIF-1α and the von Hippel–Lindau protein (pVHL), which serves as the substrate recognition subunit of an E3 ligase and is regarded as a crucial drug target in cancer, chronic anemia, and ischemia. Currently there is only one class of compounds available to interdict the HIF-1α/pVHL interaction, urging the need to discover chemical inhibitors with more diversified structures. We report here a strategy combining shape-based virtual screening and cascade docking to identify new chemical scaffolds for the designing of novel inhibitors. Based on this strategy, nine active hits have been identified and the most active hit, 9 (ZINC13466751), showed comparable activity to pVHL with an IC50 of 2.0 ± 0.14 µM, showing the great potential of utilizing these compounds for further optimization and serving as drug candidates for the inhibition of HIF-1α/von Hippel–Lindau interaction.

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Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures

Cited by 50 publications

References 31 publications

Analysis of drug–endogenous human metabolite similarities in terms of their maximum common substructures

Analysis of drug–endogenous human metabolite similarities in terms of their maximum common substructures

Quantitative Assessment of Flow Regime Alteration Using a Revised Range of Variability Methods

Discovery of novel inhibitors disrupting HIF-1α/von Hippel–Lindau interaction through shape-based screening and cascade docking

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