SPVec: A Word2vec-Inspired Feature Representation Method for Drug-Target Interaction Prediction.

ABSTRACT: Drug discovery is an academical and commercial process of global importance. Accurate identification of drug-target interactions (DTIs) can significantly facilitate the drug discovery process. Compared to the costly, labor-intensive and time-consuming experimental methods, machine learning (ML) plays an ever-increasingly important role in effective, efficient and high-throughput identification of DTIs. However, upstream feature extraction methods require tremendous human resources and expert insights, which limits the application of ML approaches. Inspired by the unsupervised representation learning methods like Word2vec, we here proposed SPVec, a novel way to automatically represent raw data such as SMILES strings and protein sequences into continuous, information-rich and lower-dimension

SUBMITTER: Zhang YF 

PROVIDER: S-EPMC6967417 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature