Predicting drug and target interaction with dilated reparameterize convolution.

ABSTRACT: Predicting drug-target interaction (DTI) stands as a pivotal and formidable challenge in pharmaceutical research. Many existing deep learning methods only learn the high-dimensional representation of ligands and targets on a small scale. However, it is difficult for the model to obtain the potential law of combining pockets or multiple binding sites on a large scale. To address this lacuna, we designed a large-kernel convolutional block for extracting large-scale sequence information and proposed a novel DTI prediction framework, named Rep-ConvDTI. The reparameterization method is introduced to help large-kernel convolutions capture small-scale information. We have also developed a gated attention mechanism to more efficiently characterize the interaction of drugs and targets. Extensive ex

SUBMITTER: Deng M 

PROVIDER: S-EPMC11747116 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature