MCF-DTI: Multi-Scale Convolutional Local–Global Feature Fusion for Drug–Target Interaction Prediction
Predicting drug–target interactions (DTIs) is a crucial step in the development of new drugs and drug repurposing. In this paper, we propose a novel drug–target prediction model called MCF-DTI. The model utilizes the SMILES representation of drugs and the sequence features of targets, employing a mu...
Saved in:
| Main Authors: | Jihong Wang, Ruijia He, Xiaodan Wang, Hongjian Li, Yulei Lu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Molecules |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1420-3049/30/2/274 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network
by: Fatema A. Albalooshi
Published: (2024-11-01) -
sEMG-Based Gesture Recognition Using Sigimg-GADF-MTF and Multi-Stream Convolutional Neural Network
by: Ming Zhang, et al.
Published: (2025-06-01) -
DTI-RME: a robust and multi-kernel ensemble approach for drug-target interaction prediction
by: Yuqing Qian, et al.
Published: (2025-07-01) -
Enhancing Direct Georeferencing Using Real-Time Kinematic UAVs and Structure from Motion-Based Photogrammetry for Large-Scale Infrastructure
by: Soohee Han, et al.
Published: (2024-12-01) -
“Involuntary Photogrammetry”: rescuing 3D geometric information from library pictures
by: Pablo Aparicio Resco, et al.
Published: (2014-05-01)