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  • 1
    Language: English
    In: Journal of chemical information and modeling, 25 March 2013, Vol.53(3), pp.553-9
    Description: Finding potent compounds for a given target in silico can be viewed as a constraint global optimization problem. This requires the use of an optimization function for which evaluations might be costly. The major task is maximizing the function while minimizing the number of evaluation steps. To solve this problem, we propose a machine learning algorithm, which first builds a statistical QSAR-model of the SAR landscape and then uses the model to identify regions in compound space having a high probability to contain a highly potent compound. For this purpose, we devise the so-called expected potency improvement (EI) criterion to rank candidate compounds with respect to their likelihood to exhibit higher potency than the most active compound in the training data. Therefore, this approach significantly differs from a purely prediction-oriented classical QSAR model. The method is superior to a nearest neighbor approach as significantly fewer evaluation steps are needed to identify the most potent compound for the given target.
    Keywords: Models, Chemical ; Drug Discovery -- Methods ; High-Throughput Screening Assays -- Methods
    ISSN: 15499596
    E-ISSN: 1549-960X
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  • 2
    Language: English
    In: Journal of chemical information and computer sciences, 2004, Vol.44(3), pp.931-9
    Description: We show that the topological polar surface area (TPSA) descriptor and the radial distribution function (RDF) applied to electronic and steric atom properties, like the conjugated electrotopological state (CETS), are the most relevant features/descriptors for predicting the human intestinal absorption (HIA) out of a large set of 2934 features/descriptors. A HIA data set with 196 molecules with measured HIA values and 2934 features/descriptors were calculated using JOELib and MOE. We used an adaptive boosting algorithm to solve the binary classification problem (AdaBoost.M1) and Genetic Algorithms based on Shannon Entropy Cliques (GA-SEC) variants as hybrid feature selection algorithms. The selection of relevant features was applied with respect to the generalization ability of the classification model, avoiding a high variance for unseen molecules (overfitting).
    Keywords: Intestinal Absorption ; Models, Theoretical
    ISSN: 0095-2338
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  • 3
    Language: English
    In: Journal of chemical information and computer sciences, 2004, Vol.44(3), pp.921-30
    Description: The paper describes different aspects of classification models based on molecular data sets with the focus on feature selection methods. Especially model quality and avoiding a high variance on unseen data (overfitting) will be discussed with respect to the feature selection problem. We present several standard approaches and modifications of our Genetic Algorithm based on the Shannon Entropy Cliques (GA-SEC) algorithm and the extension for classification problems using boosting.
    Keywords: Chemistry ; Library & Information Science;
    ISSN: 0095-2338
    E-ISSN: 15205142
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