EnGCI: enhancing GPCR-compound interaction prediction via large molecular models and KAN network
Abstract Background Identifying GPCR-compound interactions (GCI) plays a significant role in drug discovery and chemogenomics. Machine learning, particularly deep learning, has become increasingly influential in this domain. Large molecular models, due to their ability to capture detailed structural...
Saved in:
| Main Authors: | Weihao Liu, Xiaoli Li, Bo Hang, Pu Wang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-05-01
|
| Series: | BMC Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12915-025-02238-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
DropKAN: Dropout Kolmogorov–Arnold Networks
by: Mohammed Ghaith Altarabichi
Published: (2025-01-01) -
SineKAN: Kolmogorov-Arnold Networks using sinusoidal activation functions
by: Eric Reinhardt, et al.
Published: (2025-01-01) -
A Local–Global Graph KAN for Multi‐Class Prediction of PPI
by: Minghui Liu, et al.
Published: (2025-05-01) -
Application of a KAN-LSTM Fusion Model for Stress Prediction in Large-Diameter Pipelines
by: Zechao Li, et al.
Published: (2025-04-01) -
A SOH Estimation Method for Lithium-Ion Batteries Based on CPA and CNN-KAN
by: Kaixin Cheng, et al.
Published: (2025-06-01)