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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 interactions. In this paper, we extend the existing supervised learning approach – bipartite local model (BLM) by integrating a strategy for handling new drug and target candidates. Based on the assumption that similar drugs and targets have similar interaction profiles, we present a simple neighbor-based training data inferring procedure and integrate it into the frame work of BLM. This globalized BLM called bipartite local model with neighbor-based inferring (BLMN) then has an extended functionality for prediction interactions between new drug candidates and target candidates. Consistent good performance of BLMN has been observed in the experiment for the prediction of interaction between drugs and four important categories of targets. For the Nuclear Receptors dataset, where there are more chances for the presented strategy to be applied, 20% improvement in terms of AUPR has been achieved. This demonstrates the effectiveness of BLMN and its potential in drug-target interaction prediction.