GANsDTA: Predicting Drug-Target Binding Affinity Using GANs.

ABSTRACT: The computational prediction of interactions between drugs and targets is a standing challenge in drug discovery. State-of-the-art methods for drug-target interaction prediction are primarily based on supervised machine learning with known label information. However, in biomedicine, obtaining labeled training data is an expensive and a laborious process. This paper proposes a semi-supervised generative adversarial networks (GANs)-based method to predict binding affinity. Our method comprises two parts, two GANs for feature extraction and a regression network for prediction. The semi-supervised mechanism allows our model to learn proteins drugs features of both labeled and unlabeled data. We evaluate the performance of our method using multiple public datasets. Experimental results demonstr

SUBMITTER: Zhao L 

PROVIDER: S-EPMC6962343 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature