In drug discovery research, it is challenging but of great importance to be able to determine which 3-dimensional (3D) shapes (so-called conformers) of a given molecule are responsible for its observed biological activity. Due to structural flexibility, a molecule may adopt a wide range of conformers and the identification of the bioactive conformers is extremely important in order to understand the recognition mechanism between small molecules and proteins, which is crucial in drug discovery and development. The drug activity prediction is to predict the activity of proposed drug compounds by learning from the observed activity of previously-synthesized drug compounds. The most reliable approach to obtain the bioactive conformer is to use the X-ray crystal structure of a ligand-protein complex.
Below is a list of activity prediction methods
The QSAR methods are without instance-based embedding. In the QSAR methods, three widely used classification algorithms includes decision tree (DT), 1-norm SVM, and random forest.
Researcher encoded the 3-dimensional structures using pharmacophore fingerprints which are binary strings, and accomplished instance-based embedding using calculated dissimilarity distances. Four dissimilarity measures were employed and their performances were compared. 1-norm SVM was used for joint feature selection and classification. The approach was applied to four data sets, and the best proposed model for each data set was determined by using the dissimilarity measure yielding the smallest number of selected features. The proposed approach produced the best predictive models for one data set and second best predictive models for the rest of the data sets, based on the external validations.
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