MetaDTA: Meta-learning-based drug-target binding affinity prediction

We propose a meta-learning-based model for drug-target binding affinity prediction (MetaDTA), for which no information of the protein structures or binding sites is available. We formulate our method based on the Attentive Neural Processes (ANPs) (Kim et al., 2019), where the binding affinities for each target protein are modeled as a regression function of the compounds. Known drug-target binding affinity pairs are used as support set to determine the regression function. We designed few-shot prediction experiments with small number of support set data, which are similar to the typical situations in actual drug discovery processes. Experimental results showed that the proposed method outperforms the sequence-based baseline models with the same amount of limited data.