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DemQSAR: predicting human volume of distribution and clearance of drugs

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Abstract

In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure–activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds. In many cases the number of generated features exceeds the number of molecular compounds with known property values that are available for learning. Machine learning methods tend to overfit the training data in such situations, i.e. the method adjusts to very specific features of the training data, which are not characteristic for the considered property. This problem can be alleviated by diminishing the influence of unimportant, redundant or even misleading features. A better strategy is to eliminate such features completely. Ideally, a molecular property can be described by a small number of features that are chemically interpretable. The purpose of the present contribution is to provide a predictive modeling approach, which combines feature generation, feature selection, model building and control of overtraining into a single application called DemQSAR. DemQSAR is used to predict human volume of distribution (VDss) and human clearance (CL). To control overtraining, quadratic and linear regularization terms were employed. A recursive feature selection approach is used to reduce the number of descriptors. The prediction performance is as good as the best predictions reported in the recent literature. The example presented here demonstrates that DemQSAR can generate a model that uses very few features while maintaining high predictive power. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human VDss and CL is available on the webpage of DemPRED: http://agknapp.chemie.fu-berlin.de/dempred/.

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Abbreviations

VDSS :

Volume of distribution at steady state

CL:

Clearance

QSA/PR:

Quantitative structure–activity/-property relationship

GMFE:

Geometric mean fold-error

RFE:

Recursive feature elimination

CDK:

Chemistry development kit

PST:

Performance size trade-off

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Acknowledgments

The authors are grateful to the developers of the CDK library, which made it possible to make this application publicly available. We also like to acknowledge useful comments from an anonymous referee. This work was supported by the International Research Training Group (IRTG) on “Genomics and Systems Biology of Molecular Networks” (GRK1360 and GRK1772, German Research Foundation (DFG)).

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Authors and Affiliations

  1. Institute of Chemistry and Biochemistry, Freie Universität Berlin, Fabeckstrasse 36A, 14195, Berlin, Germany

    Ozgur Demir-Kavuk & Ernst-Walter Knapp

  2. Department of Medicinal Chemistry, Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, 06877, USA

    Jörg Bentzien & Ingo Muegge

Authors
  1. Ozgur Demir-Kavuk
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  2. Jörg Bentzien
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  3. Ingo Muegge
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  4. Ernst-Walter Knapp
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Corresponding author

Correspondence to Ernst-Walter Knapp.

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Demir-Kavuk, O., Bentzien, J., Muegge, I. et al. DemQSAR: predicting human volume of distribution and clearance of drugs. J Comput Aided Mol Des 25, 1121–1133 (2011). https://doi.org/10.1007/s10822-011-9496-z

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