CAT-CPI: Combining CNN and transformer to learn compound image features for predicting compound-protein interactions.

ABSTRACT: Compound-protein interaction (CPI) prediction is a foundational task for drug discovery, which process is time-consuming and costly. The effectiveness of CPI prediction can be greatly improved using deep learning methods to accelerate drug development. Large number of recent research results in the field of computer vision, especially in deep learning, have proved that the position, geometry, spatial structure and other features of objects in an image can be well characterized. We propose a novel molecular image-based model named CAT-CPI (combining CNN and transformer to predict CPI) for CPI task. We use Convolution Neural Network (CNN) to learn local features of molecular images and then use transformer encoder to capture the semantic relationships of these features. To extract protein se

SUBMITTER: Qian Y 

PROVIDER: S-EPMC9520300 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature