Consensus QSAR Modeling of Phosphor‐Containing Chiral AChE Inhibitors

Кузьмин, В. Е.; Muratov, Eugene; Artemenko, Anatoly G.; Varlamova, Ekaterina V.; Gorb, Leonid; Wang, Jing; Leszczyński, Jerzy

doi:10.1002/qsar.200860117

Cited by 49 publications

(36 citation statements)

References 42 publications

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“…Consensus QSAR modeling, i.e. , parallel development of multiple QSAR models using all pairwise combinations of different types of chemical descriptors and various machine learning techniques over single QSAR modes, has been shown to be advantageous [112, 113]. Nevertheless, no need exists of the overabundance of models in the consensus ensemble [94].…”

Section: Resultsmentioning

confidence: 99%

Tuning hERG Out: Antitarget QSAR Models for Drug Development

Braga

Alves

Silva

et al. 2014

CTMC

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Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death. As hERG safety testing is a mandatory FDA-required procedure, there is a considerable interest for developing predictive computational tools to identify and filter out potential hERG blockers early in the drug discovery process. In this study, we aimed to generate predictive and well-characterized quantitative structure–activity relationship (QSAR) models for hERG blockage using the largest publicly available dataset of 11,958 compounds from the ChEMBL database. The models have been developed and validated according to OECD guidelines using four types of descriptors and four different machine-learning techniques. The classification accuracies discriminating blockers from non-blockers were as high as 0.83–0.93 on external set. Model interpretation revealed several SAR rules, which can guide structural optimization of some hERG blockers into non-blockers. We have also applied the generated models for screening the World Drug Index (WDI) database and identify putative hERG blockers and non-blockers among currently marketed drugs. The developed models can reliably identify blockers and non-blockers, which could be useful for the scientific community. A freely accessible web server has been developed allowing users to identify putative hERG blockers and non-blockers in chemical libraries of their interest (http://labmol.farmacia.ufg.br/predherg).

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Section: Resultsmentioning

confidence: 99%

Tuning hERG Out: Antitarget QSAR Models for Drug Development

Braga

Alves

Silva

et al. 2014

CTMC

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“…Test set chemicals must be reasonably similar to some of the training set chemicals, and yet too great similarity can give an overly optimistic indication of a QSAR’s predictive capability. 85 …”

Section: History and Evolution Of Qsarmentioning

confidence: 99%

QSAR Modeling: Where Have You Been? Where Are You Going To?

et al. 2014

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Quantitative Structure-Activity Relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss: (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists towards collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

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“…One may also generate many models from the same training set but using different subset of features (F var ). Most of these approaches have been explored, but frequently used were ensemble of fixed learning algorithm with varied features and (or) varied training set (T fix Al fix F var or T var Al fix F var ) [35,36,[40][41][42][43][44][45][46][47][48]. For example, a technique that uses mixed training set and features (T var Al fix F var ) is the Random Forest [49] technique which is an ensemble of many decision trees built from a variation of sample and features.…”

Section: Introductionmentioning

confidence: 98%

Mixed learning algorithms and features ensemble in hepatotoxicity prediction

Liew

Lim

Yap

2011

J Comput Aided Mol Des

101

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Drug-induced liver injury, although infrequent, is an important safety concern that can lead to fatality in patients and failure in drug developments. In this study, we have used an ensemble of mixed learning algorithms and mixed features for the development of a model to predict hepatic effects. This robust method is based on the premise that no single learning algorithm is optimum for all modelling problems. An ensemble model of 617 base classifiers was built from a diverse set of 1,087 compounds. The ensemble model was validated internally with five-fold cross-validation and 25 rounds of y-randomization. In the external validation of 120 compounds, the ensemble model had achieved an accuracy of 75.0%, sensitivity of 81.9% and specificity of 64.6%. The model was also able to identify 22 of 23 withdrawn drugs or drugs with black box warning against hepatotoxicity. Dronedarone which is associated with severe liver injuries, announced in a recent FDA drug safety communication, was predicted as hepatotoxic by the ensemble model. It was found that the ensemble model was capable of classifying positive compounds (with hepatic effects) well, but less so on negatives compounds when they were structurally similar. The ensemble model built in this study is made available for public use.

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Consensus QSAR Modeling of Phosphor‐Containing Chiral AChE Inhibitors

Cited by 49 publications

References 42 publications

Tuning hERG Out: Antitarget QSAR Models for Drug Development

Tuning hERG Out: Antitarget QSAR Models for Drug Development

QSAR Modeling: Where Have You Been? Where Are You Going To?

Mixed learning algorithms and features ensemble in hepatotoxicity prediction

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