Several machine learning techniques were evaluated for the prediction of parameters relevant in pharmacology and drug discovery including rat and human microsomal intrinsic clearance as well as plasma protein binding represented as the fraction of unbound compound. The algorithms assessed in this study include artificial neural networks (ANN), support vector machines (SVM) with the extension for regression, kappa nearest neighbor (KNN), and Kohonen Networks. The data sets were described through a series of scalar, two- and three-dimensional descriptors including 2-D and 3-D autocorrelation, and radial distribution function. The feature sets were optimized for each data set individually for each machine learning technique using sequential forward feature selection. The data sets range from 400 to 600 compounds with experimentally determined values. Intrinsic clearance (CLint) is a measure of metabolism by cytochrome P-450 enzymes primarily in the vesicles of the smooth endoplasmic reticulum. These important enzymes contribute to the metabolism of an estimated 75% of the most frequently prescribed drugs in the U.S. The fraction of unbound compound (fu) greatly influences pharmacokinetics, efficacy, and toxicology. These fully in silico models are useful in guiding early stages of drug discovery, such as analogue prioritization prior to synthesis and biological testing while reducing costs associated with the in vitro determination of these parameters. LogP, the logarithm of the equilibrium octanol-water partition coefficient for a given substance is a metric of the hydrophobicity. This property is an important metric for drug absorption, distribution, metabolism, and excretion (ADME). In this work, models predict logP with a root mean square deviation (rmsd) of 0.99 for compounds in an independent test set. This result presents a substantial improvement over XlogP, an incremental system that achieves a rmsd of 1.41 over the same dataset.
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