Deep learning optimization for drug-target interaction prediction in COVID-19 using graphic processing unit

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...

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...

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

DeepDTA: Deep Drug-Target Binding Affinity Prediction

The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein-ligand interactions assume a continuum of binding strength values, also called...