Ali Yalabadi, Mehdi Yazdani-Jahromi, Niloofar Yousefi, Aida Tayebi, Sina Abdidizaji, Ozlem Garibay
We lifted 8 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| yazdanimehdi/fragxsitedti | canonical | 6 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| SSSRsize_filter | Ran | yazdanimehdi/fragxsitedti/macfrag.py pointer only (licence: NONE) · get_code("1f68a74343de8ab3") |
| args_to_config | Ran | yazdanimehdi/fragxsitedti/config.py pointer only (licence: NONE) · get_code("3879f30059786412") |
| collate_wrapper | Ran | yazdanimehdi/fragxsitedti/dataset.py pointer only (licence: NONE) · get_code("b93d8496a82508f6") |
| drop_path | Ran | yazdanimehdi/fragxsitedti/layers.py pointer only (licence: NONE) · get_code("87577b3ff9d32712") |
| integer_label_protein | Ran | yazdanimehdi/fragxsitedti/utils.py pointer only (licence: NONE) · get_code("19e3c8488605a6f7") |
| trunc_normal_ | Ran | yazdanimehdi/fragxsitedti/weight_init.py pointer only (licence: NONE) · get_code("c411a442f6b2dd1e") |
| mol_remove_atom_mapnumber | Not yet run | yazdanimehdi/fragxsitedti/macfrag.py pointer only (licence: NONE) · get_code("efaa7f96c77ab5e2") |
| mol_with_atom_index | Not yet run | yazdanimehdi/fragxsitedti/macfrag.py pointer only (licence: NONE) · get_code("82a7d66cd5df5a70") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Drug-Target Interaction (DTI) prediction is vital for drug discovery, yet challenges persist in achieving model interpretability and optimizing performance. We propose a novel transformer-based model, FragXsiteDTI, that aims to address these challenges in DTI prediction. Notably, FragXsiteDTI is the first DTI model to simultaneously leverage drug molecule fragments and protein pockets. Our information-rich representations for both proteins and drugs offer a detailed perspective on their interaction. Inspired by the Perceiver IO framework, our model features a learnable latent array, initially interacting with protein binding site embeddings using cross-attention and later refined through self-attention and used as a query to the drug fragments in the drug's cross-attention transformer block. This learnable query array serves as a mediator and enables seamless information translation, preserving critical nuances in drug-protein interactions. Our computational results on three benchmarking datasets demonstrate the superior predictive power of our model over several state-of-the-art models. We also show the interpretability of our model in terms of the critical components of both target proteins and drug molecules within drug-target pairs.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2311.02326")
get_code_for_paper("2311.02326")
have("2311.02326")
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