NFSA-DTI: A Novel Drug–Target Interaction Prediction Model Using Neural Fingerprint and Self-Attention Mechanism
Feiyang Liu, Huang Xu, Peng Cui, Shuo Li, Hongbo Wang, Ziye Wu, Giulio Vistoli, Josef Jampilek · 2024
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Description
Feiyang Liu and co-authors build a deep learning model that predicts whether a drug will hit a given target, in a 2024 paper. Testing every drug against every protein in a lab is impossible, so prediction carries the field. Earlier models leaned on local features from single encoders and underused global structure. They also modeled the crucial contact sites between drug and target weakly. The proposed model, NFSA-DTI, attacks both gaps at once.
A neural fingerprint extracts a molecule's global features, while a self-attention layer helps the network catch long-range links inside protein sequences. Local and global views are then integrated for the final call. Better interaction prediction shortens the list of pairs worth real experiments, which is where drug budgets actually go.
Catalog record
| CREATOR | Feiyang Liu, Huang Xu, Peng Cui, Shuo Li, Hongbo Wang, Ziye Wu, Giulio Vistoli, Josef Jampilek |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 47 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |