Incorporating chemical sub-structures and protein evolutionary information for inferring drug-target interactions.

ABSTRACT: Accumulating evidence has shown that drug-target interactions (DTIs) play a crucial role in the process of genomic drug discovery. Although biological experimental technology has made great progress, the identification of DTIs is still very time-consuming and expensive nowadays. Hence it is urgent to develop in silico model as a supplement to the biological experiments to predict the potential DTIs. In this work, a new model is designed to predict DTIs by incorporating chemical sub-structures and protein evolutionary information. Specifically, we first use Position-Specific Scoring Matrix (PSSM) to convert the protein sequence into the numerical descriptor containing biological evolutionary information, then use Discrete Cosine Transform (DCT) algorithm to extract the hidden features and i

SUBMITTER: Wang L 

PROVIDER: S-EPMC7171114 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature