Multilabel classification of drug-like molecules via max-margin conditional random fields

We present a multilabel learning approach for molecular classification, an important task in drug discovery. We use a conditional random field to model the dependencies between drug targets and discriminative training to separate correct multilabels from incorrect ones with a large margin. Efficient training of the model is ensured by conditional gradient optimization on the marginal dual polytope, using loopy belief propagation to find the steepest feasible ascent directions. In our experiments, the MMCRF method outperformed the support vector machine with state-of-the-art graph kernels on a dataset comprising of cancer inhibition potential of drug-like molecules against a large number cancer cell lines.