A review on the combination of binary classifiers in multiclass problems

Several real problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of problems with only two classes, also named binary classification problems. However, many problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass classification problems. The focus is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.

Novel insights for dihydroorotate dehydrogenase class 1A inhibitors discovery

The enzyme dihydroorotate dehydrogenase (DHODH) has been suggested as a promising target for the design of trypanocidal agents. We report here the discovery of novel inhibitors of Trypanosoma cruzi DHODH identified by a combination of virtual screening and ITC methods. Monitoring of the enzymatic reaction in the presence of selected ligands together with structural information obtained from X-ray crystallography analysis have allowed the identification and validation of a novel site of interaction (S2 site). This has provided important structural insights for the rational design of T cruzi and Leishmania major DHODH inhibitors. The most potent compound (1) in the investigated series inhibits TcDHODH enzyme with K(i)(app) value of 19.28 mu M and possesses a ligand efficiency of 0.54 kcal mol(-1) per non-H atom. The compounds described in this work are promising hits for further development. (C) 2010 Elsevier Masson SAS. All rights reserved.