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      Drug-like properties and the causes of poor solubility and poor permeability

      Journal of Pharmacological and Toxicological Methods

      Elsevier BV

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          Most cited references 15

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          Drug Discovery: A Historical Perspective

           J. Drews (2000)
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            Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 1. Prediction of intestinal absorption.

             Michael Clark (1999)
            A method for the rapid computation of polar molecular surface area (PSA) is described. It is shown that consideration of only a single conformer when computing PSA gives an excellent correlation with intestinal absorption data-as good as previously reported methods employing multiple conformers. Circumventing a time-consuming conformational analysis opens the possibility of computationally screening large numbers of compounds for problems relating to absorption prior to synthesis. The robustness of the criterion for identifying poorly absorbed compounds (PSA >/= 140 A(2)) is illustrated through its application to a diverse test set of 74 drugs. The PSA-based method is also compared to an experimental method for absorption prediction recently described in the literature.
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              Can we learn to distinguish between "drug-like" and "nondrug-like" molecules?

              We have used a Bayesian neural network to distinguish between drugs and nondrugs. For this purpose, the CMC acts as a surrogate for drug-like molecules while the ACD is a surrogate for nondrug-like molecules. This task is performed by using two different set of 1D and 2D parameters. The 1D parameters contain information about the entire molecule like the molecular weight and the the 2D parameters contain information about specific functional groups within the molecule. Our best results predict correctly on over 90% of the compounds in the CMC while classifying about 10% of the molecules in the ACD as drug-like. Excellent generalization ability is shown by the models in that roughly 80% of the molecules in the MDDR are classified as drug-like. We propose to use the models to design combinatorial libraries. In a computer experiment on generating a drug-like library of size 100 from a set of 10 000 molecules we obtain at least a 3 or 4 order of magnitude improvement over random methods. The neighborhoods defined by our models are not similar to the ones generated by standard Tanimoto similarity calculations. Therefore, new and different information is being generated by our models, and so it can supplement standard diversity approaches to library design.
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                Author and article information

                Journal
                Journal of Pharmacological and Toxicological Methods
                Journal of Pharmacological and Toxicological Methods
                Elsevier BV
                10568719
                July 2000
                July 2000
                : 44
                : 1
                : 235-249
                Article
                10.1016/S1056-8719(00)00107-6
                © 2000

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