During the drug development process, it is common to carry out toxicity tests and adverse effect studies, which are essential to guarantee patient safety and the success of the research. The use of in silico quantitative structure−activity relationship (QSAR) approaches for this task involves processing a huge amount of data that, in many cases, have an imbalanced distribution of active and inactive samples. This is usually termed the class-imbalance problem and may have a significant negative effect on the performance of the learned models. The performance of feature selection (FS) for QSAR models is usually damaged by the classimbalance nature of the involved datasets. This paper proposes the use of an FS method focused on dealing with the class-imbalance problems. The method is based on the use of FS ensembles constructed by boosting and using two well-known FS methods, fast clustering-based FS and the fast correlation-based filter. The experimental results demonstrate the efficiency of the proposal in terms of the classification performance compared to standard methods. The proposal can be extended to other FS methods and applied to other problems in cheminformatics.

The maximum common property similarity (MCPhd) method is presented using descriptors as a new approach to determine the similarity between two chemical compounds or molecular graphs. This method uses the concept of maximum common property arising from the concept of maximum common substructure and is based on the electrotopographic state index for atoms. A new algorithm to quantify the similarity values of chemical structures based on the presented maximum common property concept is also developed in this paper. To verify the validity of this approach, the similarity of a sample of compounds with antimalarial activity is calculated and compared with the results obtained by four different similarity methods: the small molecule subgraph detector (SMSD), molecular fingerprint based (OBabel_FP2), ISIDA descriptors and shape-feature similarity (SHAFTS). The results obtained by the MCPhd method differ significantly from those obtained by the compared methods, improving the quantification of the similarity. A major advantage of the proposed method is that it helps to understand the analogy or proximity between physicochemical properties of the molecular fragments or subgraphs compared with the biological response or biological activity. In this new approach, more than one property can be potentially used. The method can be considered a hybrid procedure because it combines descriptor and the fragment approaches.

The purpose of this work is the definition and evaluation of both atomic and local new hybrid indices. Inspired by the Refractotopological State Index for Atoms, the new atomic indices are theoretically supported by graph theory principles. The local indices, named Descriptor Centres (DCs), are obtained from the sum of the atomic values of the atoms in the selected group. Different classifiers were used for structure-activity relationship (SAR) studies, including multilayer perceptron (MLP), support vector machines (SVM) and meta-classifiers. Prediction with SVM and MLP was around 60%, but the best result was obtained with the meta-classifiers, bagging, decorate and others, with more than 92% accurate prediction. These new hybrid descriptors derived from the Refractotopological State Index for Atoms show a low mutual correlation coefficient. The same behaviour is found in the analogously defined Descriptors Centres. The best results are obtained with the inclusion of the distance between DCs with the use of meta-classifiers.