Comparative Study of Classification Algorithms Using Molecular Descriptors in Toxicological DataBases

Pereira, Max; Costa, Vítor Santos; Camacho, Rui; Fonseca, Nuno A.; Simões, Carlos J. V.; Brito, Rui M. M.

doi:10.1007/978-3-642-03223-3_11

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…The results by Pereira et al ( [32]), obtained using Weka, are included in the last column of the table for an easy comparison.…”

Section: Resultsmentioning

confidence: 99%

“…Toxicity results using the CPDBAS, NCTRER and DBPCAN data sets were published by Pereira et al in [32]. In that study only propositional algorithms from Weka [33] were evaluated.…”

Section: Similarity Bias Studymentioning

confidence: 99%

“…To assess the usefulness of these two relational learners we have used Aleph alone comparing the accuracy results with the propositional learners of [32] and the comprehensibility of the constructed models. We have used Moss to discover interesting graph fragments.…”

Section: Relational Learners Studymentioning

confidence: 99%

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A Relational Learning Approach to Structure-Activity Relationships in Drug Design Toxicity Studies

Camacho

Pereira

Costa

et al. 2011

Journal of Integrative Bioinformatics

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SummaryIt has been recognized that the development of new therapeutic drugs is a complex and expensive process. A large number of factors affect the activity in vivo of putative candidate molecules and the propensity for causing adverse and toxic effects is recognized as one of the major hurdles behind the current "target-rich, lead-poor" scenario. Structure-Activity Relationship (SAR) studies, using relational Machine Learning (ML) algorithms, have already been shown to be very useful in the complex process of rational drug design. Despite the ML successes, human expertise is still of the utmost importance in the drug development process. An iterative process and tight integration between the models developed by ML algorithms and the know-how of medicinal chemistry experts would be a very useful symbiotic approach. In this paper we describe a software tool that achieves that goal -iLogCHEM. The tool allows the use of Relational Learners in the task of identifying molecules or molecular fragments with potential to produce toxic effects, and thus help in stream-lining drug design in silico. It also allows the expert to guide the search for useful molecules without the need to know the details of the algorithms used. The models produced by the algorithms may be visualized using a graphical interface, that is of common use amongst researchers in structural biology and medicinal chemistry. The graphical interface enables the expert to provide feedback to the learning system. The developed tool has also facilities to handle the similarity bias typical of large chemical databases. For that purpose the user can filter out similar compounds when assembling a data set. Additionally, we propose ways of providing background knowledge for Relational Learners using the results of Graph Mining algorithms.

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“…The results by Pereira et al ( [32]), obtained using Weka, are included in the last column of the table for an easy comparison.…”

Section: Resultsmentioning

confidence: 99%

“…Toxicity results using the CPDBAS, NCTRER and DBPCAN data sets were published by Pereira et al in [32]. In that study only propositional algorithms from Weka [33] were evaluated.…”

Section: Similarity Bias Studymentioning

confidence: 99%

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A Relational Learning Approach to Structure-Activity Relationships in Drug Design Toxicity Studies

Camacho

Pereira

Costa

et al. 2011

Journal of Integrative Bioinformatics

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“…The development of new drugs is an expensive and complex process, and it has many steps ( Pereira et al, 2009 ). One of the main steps in this process is the toxicity assessment of drugs' components.…”

Section: Introductionmentioning

confidence: 99%

Neutrosophic rule-based prediction system for toxicity effects assessment of biotransformed hepatic drugs

Basha

Tharwat

Abdalla

et al. 2019

Expert Systems with Applications

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a b s t r a c tMeasuring toxicity is an important step in drug development. However, the current experimental methods which are used to estimate the drug toxicity are expensive and need high computational efforts. Therefore, these methods are not suitable for large-scale evaluation of drug toxicity. As a consequence, there is a high demand to implement computational models that can predict drug toxicity risks. In this paper, we used a dataset that consists of 553 drugs that biotransformed in the liver. In this data, there are four toxic effects, namely, mutagenic, tumorigenic, irritant and reproductive effects. Each drug is represented by 31 chemical descriptors. This paper proposes two models for predicting drug toxicity risks. The proposed models consist of three phases. In the first phase, the most discriminative features are selected using rough set-based methods to reduce the classification time and improve the classification performance. In the second phase, three different sam pling algorithms, namely, Random Under-Sampling, Random Over-Sampling, and Synthetic Minority Oversampling Technique (SMOTE) are used to obtain balanced data. In the third phase, the first proposed model employs the Neutrosophic Rule-based Classification System (NRCS), and the second model uses Genetic NRCS (GNRCS) to classify an unknown drug into toxic or non-toxic. The experimental results proved that the proposed models obtained high sensitivity (89-93%), specificity (91-97%), and GM (90-94%) for all toxic effects. Overall, the results of the proposed models indicate that it could be utilized for the prediction of drug toxicity in the early stages of drug development.

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“…The development of novel drugs is a complex and an expensive process, and it has several steps1. Measuring toxicity of the drugs’ components is one of these steps.…”

mentioning

confidence: 99%

A Predictive Model for Toxicity Effects Assessment of Biotransformed Hepatic Drugs Using Iterative Sampling Method

Tharwat

Moemen

Hassanien

2016

Sci Rep

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Measuring toxicity is one of the main steps in drug development. Hence, there is a high demand for computational models to predict the toxicity effects of the potential drugs. In this study, we used a dataset, which consists of four toxicity effects:mutagenic, tumorigenic, irritant and reproductive effects. The proposed model consists of three phases. In the first phase, rough set-based methods are used to select the most discriminative features for reducing the classification time and improving the classification performance. Due to the imbalanced class distribution, in the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling and Synthetic Minority Oversampling Technique are used to solve the problem of imbalanced datasets. ITerative Sampling (ITS) method is proposed to avoid the limitations of those methods. ITS method has two steps. The first step (sampling step) iteratively modifies the prior distribution of the minority and majority classes. In the second step, a data cleaning method is used to remove the overlapping that is produced from the first step. In the third phase, Bagging classifier is used to classify an unknown drug into toxic or non-toxic. The experimental results proved that the proposed model performed well in classifying the unknown samples according to all toxic effects in the imbalanced datasets.

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Comparative Study of Classification Algorithms Using Molecular Descriptors in Toxicological DataBases

Cited by 7 publications

References 26 publications

A Relational Learning Approach to Structure-Activity Relationships in Drug Design Toxicity Studies

A Relational Learning Approach to Structure-Activity Relationships in Drug Design Toxicity Studies

Neutrosophic rule-based prediction system for toxicity effects assessment of biotransformed hepatic drugs

A Predictive Model for Toxicity Effects Assessment of Biotransformed Hepatic Drugs Using Iterative Sampling Method

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