Machine Learning for Drug-Target Interaction Prediction

Globalized Bipartite Local Learning Model for Drug-Target Interaction Prediction

Computational methods provide efficient ways to predict possible interactions between drugs and targets, which is critical in drug discovery. Supervised prediction with bipartite Local Model recently has been shown to be effective for prediction of drug-target interactions. However, this pure “local” model is unapplicable to new drug or target candidates that currently have no known interaction...

Similarity based learning method for drug target interaction prediction

Drug-target interaction prediction using ensemble learning and dimensionality reduction.

Experimental prediction of drug-target interactions is expensive, time-consuming and tedious. Fortunately, computational methods help narrow down the search space for interaction candidates to be further examined via wet-lab techniques. Nowadays, the number of attributes/features for drugs and targets, as well as the amount of their interactions, are increasing, making these computational metho...

Deep Learning for Drug Target Prediction

An important computational tool in drug design is target prediction where either for a given chemical structure the interacting biomolecules (e.g. proteins) must be identified. Chemical structures interact with different biomolecules if they have similar 3D structure. Thus, the outputs of the prediction are highly interdependent from each other. Furthermore, we have partially labelled molecules...

Two Different Machine Learning Techniques for Drug-Drug Interaction Extraction

Detection of drug-drug interaction (DDI) is an important task for both patient safety and efficient health care management. In this paper, we explore the combination of two different machine-learning approaches to extract DDI: (i) a feature-based method using a SVM classifier with a set of features extracted from texts, and (ii) a kernelbased method combining 3 different kernels. Experiments co...