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Centro de Química Farmacéutica. Havana, Cuba.

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.