Molecular fingerprint-derived similarity measures for toxicological read-across: Recommendations for optimal use

Mellor, Claire L.; Robinson, Richard L. Marchese; Benigni, Romualdo; Ebbrell, David J.; Enoch, Steven J.; Firman, James W.; Madden, Judith C.; Pawar, Gopal; Yang, Chihae; Cronin, Mark T.D.

doi:10.1016/j.yrtph.2018.11.002

Cited by 69 publications

(64 citation statements)

References 58 publications

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“…The notion of chemical similarity plays an important role in predicting the properties of chemical compounds, clustering chemicals, and in particular, in conducting functional analysis studies. The calculation of the similarity of any two molecules is achieved by comparing their molecular fingerprints (32). These fingerprints are comprised of structural information about the molecule which has been encoded as a series of bits.…”

Section: Structural and Functional Analysismentioning

confidence: 99%

A comprehensive structural and functional analysis of the ligand binding domain of the nuclear receptor superfamily reveals highly conserved signaling motifs and two distinct canonical forms through evolution

et al. 2020

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Nuclear receptors (NRs) are transcriptional factors that play an essential role in all aspects of human development, metabolism and physiology. A prime example of a NR is the glucocorticoid receptor (GR). Structure-wise, the GR is typical of the NR superfamily, while its signaling is a part of multiple physiological mechanisms. In this study, using the GR and the steroid hormone receptors as a basis, an analysis of the structure, function and evolution of the NR ligand binding domain was conducted, while a list of NR mutations was composed in order to examine the effects of the mutations on NR structure and function. The results proposed 7 conserved signaling motifs and identified the amino acid repeating pattern 'LxxLL' or 'LLxxL' in the ligand binding domains (LBDs) of the NRs. Phylogenetic analysis revealed 4 distinct monophyletic branches, and it proposed new evolutionary relations between the LBD of NRs. Furthermore, structural and functional comparisons through NR LBD structures and their corresponding ligands displayed two major canonical forms, one for the steroid hormone-like cluster and another one for the thyroid hormone-like cluster. Last but not least, a new sub-cluster of estrogen receptor α with a specific canonical form has been identified. Although this sub-cluster has 98% similarity in sequence level with all known ERα, shows more significant structural similarity with the ERβ members (RMSD <2Å) rather than the ERα. In particular, the Y537S mutation, which is very common in breast cancer, creates this new transform of ERα'. ERα' is functionally and structurally more similar to ERβ, while still retaining some of its ERα characteristics. This new information may be of high importance in order to understand the signaling mechanisms underlying NRs and cancer.

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Section: Structural and Functional Analysismentioning

confidence: 99%

A comprehensive structural and functional analysis of the ligand binding domain of the nuclear receptor superfamily reveals highly conserved signaling motifs and two distinct canonical forms through evolution

et al. 2020

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“…In the best case, the occurrence of a common scaffold makes it possible to analyze the influence of different substituents on the activity, allowing for a thorough assessment. Chemical similarity can be defined by calculating the similarity of feature vectors such as chemical properties or fingerprints using similarity metrics such as the Euclidean or Tanimoto similarity, opening up numerous possibilities for the calculation …”

Section: Expert Methodsmentioning

confidence: 99%

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

Hemmerich

Ecker

2020

WIREs Comput Mol Sci

121

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In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Chemoinformatics

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“…A Tanimoto score cutoff of 0.7 generally reflects high similarity of core structure. 13 Classes are then clustered using K-medoids. 30 Clustering based on the Tanimoto structural similarity score is performed using Pipeline Pilot.…”

Section: Methods For Clustering a Chemical Inventorymentioning

confidence: 99%

“… 11 All of these approaches use Tanimoto or other structural similarity scores 12 as a rudimentary basis to identify similar chemicals calculated from either SMILES or fingerprints. 13 The main drawback of clustering an inventory based on such scores is that the substructural features, which can affect toxicodynamic and toxicokinetic properties, are not weighted according to their impact on the toxicity. Consequently, these methods typically produce clusters with divergent substructural features that may confer dramatically different toxicokinetic and toxicodynamic properties.…”

Section: Introductionmentioning

confidence: 99%

Clustering a Chemical Inventory for Safety Assessment of Fragrance Ingredients: Identifying Read-Across Analogs to Address Data Gaps

Date

O’Brien

Botelho

et al. 2020

Chem. Res. Toxicol.

462

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A valuable approach to chemical safety assessment is the use of read-across chemicals to provide safety data to support the assessment of structurally similar chemicals. An inventory of over 6000 discrete organic chemicals used as fragrance materials in consumer products has been clustered into chemical class-based groups for efficient search of read-across sources. We developed a robust, tiered system for chemical classification based on (1) organic functional group, (2) structural similarity and reactivity features of the hydrocarbon skeletons, (3) predicted or experimentally verified Phase I and Phase II metabolism, and (4) expert pruning to consider these variables in the context of specific toxicity end points. The systematic combination of these data yielded clusters, which may be visualized as a top-down hierarchical clustering tree. In this tree, chemical classes are formed at the highest level according to organic functional groups. Each subsequent subcluster stemming from classes in this hierarchy of the cluster is a chemical cluster defined by common organic functional groups and close similarity in the hydrocarbon skeleton. By examining the available experimental data for a toxicological endpoint within each cluster, users can better identify potential read-across chemicals to support safety assessments.

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Molecular fingerprint-derived similarity measures for toxicological read-across: Recommendations for optimal use

Abstract: This is a repository copy of Molecular fingerprint-derived similarity measures for toxicological read-across: Recommendations for optimal use.

Cited by 69 publications

References 58 publications

A comprehensive structural and functional analysis of the ligand binding domain of the nuclear receptor superfamily reveals highly conserved signaling motifs and two distinct canonical forms through evolution

A comprehensive structural and functional analysis of the ligand binding domain of the nuclear receptor superfamily reveals highly conserved signaling motifs and two distinct canonical forms through evolution

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

Clustering a Chemical Inventory for Safety Assessment of Fragrance Ingredients: Identifying Read-Across Analogs to Address Data Gaps

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