Similarity searching of chemical databases using atom environment descriptors: evaluation of performance

A molecular similarity searching technique based on Atom Environments, information-gain based feature selection and the Naïve Bayesian Classifier has been applied to a series of diverse datasets and its performance compared to alternative searching methods. In this application, using a recently published dataset of more than 100,000 molecules from the MDL Drug Data Report (MDDR) database, the Atom Environment approach appears to outperform fusion of ranking scores as well as binary kernel discrimination, which are both used in combination with Unity fingerprints. Overall retrieval rates among the top 5% of the sorted library are nearly 10% better (more than 14% better in relative numbers) than the second best method, Unity fingerprints and binary kernel discrimination. In 10 out of 11 sets of active compounds the combination of Atom Environments and the Naïve Bayesian Classifier appears to be the superior method while in the remaining data set, data fusion and binary kernel discrimination in combination with Unity fingerprints are the method of choice. Binary kernel discrimination in combination with Unity fingerprints generally comes second in performance overall. The difference in performance can largely be attributed to the different molecular descriptors used. Atom Environments outperform Unity fingerprints by a large margin if the combination of these descriptors with the Tanimoto coefficient is compared. The Naïve Bayesian Classifier in combination with information-gain based feature selection and selection of a sensible number of features performs about as well as binary kernel discrimination in experiments where these classification methods are compared. When used on a monoaminooxidase data set, Atom Environments and the Naïve Bayesian perform as well as binary kernel discrimination in the case of a 50/50 split of training and test compounds. In the case of sparse training data, binary kernel discrimination is found to be superior on this particular data set. On a third data set, the Atom Environment descriptor is again shown to be superior to other 2D fingerprints when used in combination with the Tanimoto similarity coefficient. Feature selection is shown to be a crucial step in determining the performance of the algorithm. The representation of molecules by Atom Environments is found to be more effective than Unity fingerprints for the type of biological receptor similarity calculations examined here. Combining information prior to scoring, as in the Bayesian Classifier and binary kernel discrimination, is found to be superior to posterior data fusion (in the datasets tested here). Brief: A novel fast …