Drug discovery and mechanism prediction with explainable graph neural networks
Abstract Apprehension of drug action mechanism is paramount for drug response prediction and precision medicine. The unprecedented development of machine learning and deep learning algorithms has expedited the drug response prediction research. However, existing methods mainly focus on forward encod...
Saved in:
| Main Authors: | Conghao Wang, Gaurav Asok Kumar, Jagath C. Rajapakse |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-83090-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Sparse Deep Neural Network for Encoding and Decoding the Structural Connectome
by: Satya P. Singh, et al.
Published: (2024-01-01) -
BetaExplainer: A Probabilistic Method to Explain Graph Neural Networks
by: Whitney Sloneker, et al.
Published: (2025-06-01) -
Graph neural network-based drug-drug interaction prediction
by: Khushnood Abbas, et al.
Published: (2025-08-01) -
Reliable and Faithful Generative Explainers for Graph Neural Networks
by: Yiqiao Li, et al.
Published: (2024-12-01) -
Explainable Graph Neural Networks for Power Grid Fault Detection
by: Richard Bosso, et al.
Published: (2025-01-01)