Centro de Química Farmacéutica. Havana, Cuba. 

http://www.cqf.sld.cu/

Prediction of physicochemical properties is of major concern for pharmaceutical research. In this context, machine learning methods are of great importance due to their contribution to the development of a plethora of models. In particular, we are working on a novel framework for physicochemical property prediction, where training data is first clustered according to their structural similarity, and a classifier is trained for each conformed cluster. In this regard, the property prediction of a novel candidate drug is modeled by the classifier associated with the cluster that has more structurally-similar compounds with regard to the new putative drug.