Effective Feature Selection Method for Class-Imbalance Datasets Applied to Chemical Toxicity Prediction.

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 class-imbalance 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.

Maximum common property: a new approach for molecular similarity.

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.

Similitud molecular empleando Índices Híbridos / Molecular similarity using Hybrid Indices

Se presenta un método para la detección de semejanza entre moléculas basado en macheo inexacto de grafos. Se parte del grafo molecular completo ponderado en sus vértices por propiedades químico-físicas particionadas sobre los mismos, se reduce el grafo por el procedimiento CALEDE que define Centros Descriptores o fragmentos de primer orden, los cuales son subgrafos ponderados por la suma de los valores de los vértices ponderados individualmente a su vez, y se construyen fragmentos denominados de segundo orden que incluyen la distancia entre los centros de masas de ambos centros descriptores. Se presenta el método de búsqueda aplicado a una base de datos de más de 300 moléculas con sus respectivas estructuras en tres dimensiones. Esos compuestos se encuentran evaluados como anticancerígenos en la base de datos de compuestos del NCBI-USA. En el experimento computacional se encuentra que, en dependencia de la función de similitud empleada, es posible detectar compuestos que a pesar de poseer diferente topología, poseen valores de las propiedades empleadas para el macheo lo cual sugiere la presencia de potenciales farmacóforos como hallazgo relevante, lo cual constituiría un novedoso enfoque para el diseño computacional de fármacos(AU)

A method for detecting similarity between molecules based on inexact matching graph is presented. We start from a complete molecular graph vertices weighted by several hybrid indices. The molecular graph is reduced by CALEDE procedure, which define descriptors centers or first order fragments. These fragments are subgraphs weighted with the sum of values of the vertices weighted with the hybrid indices. It also define second order fragments by including the distance between the centers of mass of both descriptors centers. The search method applied to a database of over 300 molecules with their respective threedimensional structures is presented. These compounds are reported in the NCBI-USA database of compounds whish were evaluated in anticancer tests. In the computational experiment, depending on the similarity function used, is possible to detect compounds that despite having different topology have property values suggesting the presence of potential pharmacophore. It suggest the possibility to use this approach as a novel approach for computational drug design(AU)