Fragment-based prediction of skin sensitization using recursive partitioning

Lü, Jing; Zheng, Mingyue; Wang, Yong; Shen, Qiancheng; Luo, Xiaomin; Jiang, Hualiang; Chen, Kaixian

doi:10.1007/s10822-011-9472-7

Cited by 10 publications

(9 citation statements)

References 39 publications

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“…Taken together, these issues often lead to the neglect or insufficient definition of the AD of models, which is not only problematic for the application of the individual models but also for the perception and reputation of computational methods in general. Lu et al (2011) used recursive partitioning to derive a decision tree model for the binary classification of sensitizers and non-sensitizers based on LLNA data for 295 compounds, including, among others, Michael acceptors, S N 2 and S N Ar electrophiles, Schiff base formers, and acyl transfer agents. Eight quantum chemical and physicochemical descriptors linked to chemical reactivity, hydrophobicity, and electrostatic interaction, as well as a fragment descriptor, served as the input for model building.…”

Section: Nonlinear Modelsmentioning

confidence: 99%

Computational approaches for skin sensitization prediction

Wilm

Kühnl

Kirchmair

2018

Critical Reviews in Toxicology

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Drugs, cosmetics, preservatives, fragrances, pesticides, metals, and other chemicals can cause skin sensitization. The ability to predict the skin sensitization potential and potency of substances is therefore of enormous importance to a host of different industries, to customers' and workers' safety. Animal experiments have been the preferred testing method for most risk assessment and regulatory purposes but considerable efforts to replace them with non-animal models and in silico models are ongoing. This review provides a comprehensive overview of the computational approaches and models that have been developed for skin sensitization prediction over the last 10 years. The scope and limitations of rule-based approaches, read-across, linear and nonlinear (quantitative) structure-activity relationship ((Q)SAR) modeling, hybrid or combined approaches, and models integrating computational methods with experimental results are discussed followed by examples of relevant models. Emphasis is placed on models that are accessible to the scientific community, and on model validation. A dedicated section reports on comparative performance assessments of various approaches and models. The review also provides a concise overview of relevant data sources on skin sensitization.

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Section: Nonlinear Modelsmentioning

confidence: 99%

Computational approaches for skin sensitization prediction

Wilm

Kühnl

Kirchmair

2018

Critical Reviews in Toxicology

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“…164 Structural fragments with skin sensitization potential were extracted using Gaston and then combined with other descriptors to construct a recursive partitioning tree for classification of lymph assay data. 165 Wang et al employed Gaston to extract SAs from carcinogenicity data. 166 They adapted Gaston so that redundant fragments were automatically pruned and combined the remaining fragments into Molecular Fragment Trees using a statistical method.…”

Section: Applicationsmentioning

confidence: 99%

Perspectives on Knowledge Discovery Algorithms Recently Introduced in Chemoinformatics: Rough Set Theory, Association Rule Mining, Emerging Patterns, and Formal Concept Analysis

Gardiner

Gillet

2015

J. Chem. Inf. Model.

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Knowledge Discovery in Databases (KDD) refers to the use of methodologies from machine learning, pattern recognition, statistics, and other fields to extract knowledge from large collections of data, where the knowledge is not explicitly available as part of the database structure. In this paper, we describe four modern data mining techniques, Rough Set Theory (RST), Association Rule Mining (ARM), Emerging Pattern Mining (EP), and Formal Concept Analysis (FCA), and we have attempted to give an exhaustive list of their chemoinformatics applications. One of the main strengths of these methods is their descriptive ability. When used to derive rules, for example, in structure-activity relationships, the rules have clear physical meaning. This review has shown that there are close relationships between the methods. Often apparent differences lie in the way in which the problem under investigation has been formulated which can lead to the natural adoption of one or other method. For example, the idea of a structural alert, as a structure which is present in toxic and absent in nontoxic compounds, leads to the natural formulation of an Emerging Pattern search. Despite the similarities between the methods, each has its strengths. RST is useful for dealing with uncertain and noisy data. Its main chemoinformatics applications so far have been in feature extraction and feature reduction, the latter often as input to another data mining method, such as an Support Vector Machine (SVM). ARM has mostly been used for frequent subgraph mining. EP and FCA have both been used to mine both structural and nonstructural patterns for classification of both active and inactive molecules. Since their introduction in the 1980s and 1990s, RST, ARM, EP, and FCA have found wide-ranging applications, with many thousands of citations in Web of Science, but their adoption by the chemoinformatics community has been relatively slow. Advances, both in computer power and in algorithm development, mean that there is the potential to apply these techniques to larger data sets and thus to different problems in the future.

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“…To understand the structural and physicochemical factors responsible for potent APN inhibitory activity, two different QSAR modeling techniques, namely Bayesian modeling [28] and recursive partitioning (RP) modeling [29], were performed. These methods rely only on the activity profile of these compounds (active or inactive) and not on the numerical experimental data.…”

Section: Qsar Analysismentioning

confidence: 99%

Exploration of structural and physicochemical requirements and search of virtual hits for aminopeptidase N inhibitors

2013

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Aminopeptidase N (APN) inhibitors have been reported to be effective in treating of life threatening diseases including cancer. Validated ligand- and structure-based pharmacophore mapping approaches were combined with Bayesian modeling and recursive partitioning to identify structural and physicochemical requirements for highly active APN inhibitors. Based on the assumption that ligand- and structure-based pharmacophore models are complementary, the efficacy of 'multiple pharmacophore screening' for filtering true positive virtual hits was investigated. These multiple pharmacophore screening methods were utilized to search novel virtual hits for APN inhibition. The number of hits was refined and reduced by recursive partitioning, drug-likeliness, pharmacokinetic property prediction, and comparative molecular-docking studies. Four compounds were proposed as the potential virtual hits for APN enzyme inhibition.

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Fragment-based prediction of skin sensitization using recursive partitioning

Cited by 10 publications

References 39 publications

Computational approaches for skin sensitization prediction

Computational approaches for skin sensitization prediction

Perspectives on Knowledge Discovery Algorithms Recently Introduced in Chemoinformatics: Rough Set Theory, Association Rule Mining, Emerging Patterns, and Formal Concept Analysis

Exploration of structural and physicochemical requirements and search of virtual hits for aminopeptidase N inhibitors

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