(Q) SAR study on the metabolic stability of steroidal androgens

Bursi, Roberta; Gooyer, Marcel E. de; Grootenhuis, A. J.; Jacobs, Peter L; Louw, Jaap van der; Leysen, Dirk

doi:10.1016/s1093-3263(01)00089-4

Cited by 20 publications

(10 citation statements)

References 30 publications

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“…Prediction of metabolic stability by machine learning was first described by Bursi et al in 2001, for a data set of 32 in-house steroidal androgens using the C5.0 decision tree [4]. Prediction of the metabolic turnover rate in human S9 homogenate has also been reported for 631 proprietary compounds using the kNN QSPR method [5].…”

Section: Introductionmentioning

confidence: 99%

Predicting human liver microsomal stability with machine learning techniques

Sakiyama

Yuki

Moriya

et al. 2008

Journal of Molecular Graphics and Modelling

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Section: Introductionmentioning

confidence: 99%

Predicting human liver microsomal stability with machine learning techniques

Sakiyama

Yuki

Moriya

et al. 2008

Journal of Molecular Graphics and Modelling

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“…An early metabolic stability model for 32 steroidal androgens was described by Bursi et al [116]. Using a decision tree approach with very simple descriptors, the authors could differentiate between compounds with high and low metabolic stability.…”

Section: Microsomal Stabilitymentioning

confidence: 99%

Machine Learning Techniques for In Silico Modeling of Drug Metabolism

Fox

Kriegl²

2006

CTMC

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The computational assessment of drug metabolism has gained considerable interest in pharmaceutical research. Amongst others, machine learning techniques have been employed to model relationships between the chemical structure of a compound and its metabolic fate. Examples for these techniques, which were originally developed in fields far from drug discovery, are artificial neural networks or support vector machines. This paper summarizes the application of various machine learning techniques to predict the interaction between organic molecules and metabolic enzymes. More complex endpoints such as metabolic stability or in vivo clearance will also be addressed. It is shown that the prediction of metabolic endpoints with machine learning techniques has made considerable progress over the past few years. Depending on the procedure used, either classification or quantitative prediction is possible for even large and diverse compound sets. Together with the expanding experimental data basis, these in silico methods have become valuable tools in the drug discovery and development process.

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“…To date, research predicting drug metabolism has been limited to a number of technologies, such as rule-based tools and algorithms for sites of metabolism [7], electronic models [8,9], homology models [10][11][12], as well as pharmacophores and QSARs for K m values [13][14][15]. In general, the data sets that the QSAR models use are severely limited in both size and structural diversity.…”

Section: Role Of Computational Approaches For Drug Metabolismmentioning

confidence: 99%

In silico approaches to predicting drug metabolism, toxicology and beyond

Ekins

2003

Biochemical Society Transactions

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The discovery and optimization of new drug candidates is becoming increasingly reliant upon the combination of experimental and computational approaches related to drug metabolism, toxicology and general biopharmaceutical properties. With the considerable output of high-throughput assays for cytochrome-P450-mediated drug-drug interactions, metabolic stability and assays for toxicology, we have orders of magnitude more data that will facilitate model building. A recursive partitioning model for human liver microsomal metabolic stability based on over 800 structurally diverse molecules was used to predict molecules with known log in vitro clearance data (Spearman's rho -0.64, P <0.0001). In addition, with solely published data, a quantitative structure-activity relationship for 66 inhibitors of the potassium channel human ether-a-gogo (hERG) that has been implicated in the failure of a number of recent drugs has been generated. This model has been validated with further published data for 25 molecules (Spearman's rho 0.83, P <0.0001). If continued value is to be realized from these types of computational models, there needs to be some applied research on their validation and optimization with new data. Some relatively simple approaches may have value when it comes to combining data from multiple models in order to improve and focus drug discovery on the molecules most likely to succeed.

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(Q) SAR study on the metabolic stability of steroidal androgens

Cited by 20 publications

References 30 publications

Predicting human liver microsomal stability with machine learning techniques

Predicting human liver microsomal stability with machine learning techniques

Machine Learning Techniques for In Silico Modeling of Drug Metabolism

In silico approaches to predicting drug metabolism, toxicology and beyond

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