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Discriminating Drug-Like Compounds by Partition Trees with Quantum Similarity Indices and Graph Invariants

[ Vol. 22 , Issue. 34 ]


Jesus V. de Julián-Ortiz, Rafael Gozalbes and Emili Besalú   Pages 5179 - 5195 ( 17 )


The search for new drug candidates in databases is of paramount importance in pharmaceutical chemistry. The selection of molecular subsets is greatly optimized and much more promising when potential drug-like molecules are detected a priori. In this work, about one hundred thousand molecules are ranked following a new methodology: a drug/non-drug classifier constructed by a consensual set of classification trees. The classification trees arise from the stochastic generation of training sets, which in turn are used to estimate probability factors of test molecules to be drug-like compounds. Molecules were represented by Topological Quantum Similarity Indices and their Graph Theoretical counterparts. The contribution of the present paper consists of presenting an effective ranking method able to improve the probability of finding drug-like substances by using these types of molecular descriptors.


Drug-like molecules, Recursive partition trees, Topological quantum similarity indices, Topological indices, Consensus model.


Institut de Química Computacional i Catàlisi, Universitat de Girona, 17071 Girona, Spain.

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