A Hybrid Approach Based on Self-Organizing Neural Networks and the K-Nearest Neighbors Method to Study Molecular Similarity

Abdelmalek Amine, Zakaria Elberrichi, Michel Simonet and Ali Rahmouni
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Abdelmalek Amine: Tahar Moulay University, Algeria
Zakaria Elberrichi: Djillali Liabes University, Algeria
Michel Simonet: Fourier University, France
Ali Rahmouni: Tahar Moulay University, Algeria

International Journal of Chemoinformatics and Chemical Engineering (IJCCE), 2011, vol. 1, issue 1, 75-95

Abstract: The “Molecular Similarity Principle” states that structurally similar molecules tend to have similar properties—physicochemical and biological. The question then is how to define “structural similarity” algorithmically and confirm its usefulness. Within this framework, research by similarity is registered, which is a practical approach to identify molecule candidates (to become drugs or medicines) from databases or virtual chemical libraries by comparing the compounds two by two. Many statistical models and learning tools have been developed to correlate the molecules’ structure with their chemical, physical or biological properties. The role of data mining in chemistry is to evaluate “hidden” information in a set of chemical data. Each molecule is represented by a vector of great dimension (using molecular descriptors), the applying a learning algorithm on these vectors. In this paper, the authors study the molecular similarity using a hybrid approach based on Self-Organizing Neural Networks and Knn Method.

Date: 2011
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