

# Otter DUDe Dataset Card
Otter DUDe includes 1,452,568 instances of drug-target interactions.

## Dataset details
#### DUDe 
DUDe comprises a collection of 22,886 active compounds and their corresponding affinities towards 102 targets. For our study, we utilized a preprocessed version of the DUDe, which includes 1,452,568 instances of drug-target interactions. To prevent any data leakage, we eliminated the negative interactions and the overlapping triples with the TDC DTI dataset. As a result, we were left with a total of 40,216 drug-target interaction pairs.

**Original dataset:**
- Citation: Samuel Sledzieski, Rohit Singh, Lenore Cowen, and Bonnie Berger. Adapting protein language models for rapid dti prediction. bioRxiv, pages 2022–11, 2022

**Paper or resources for more information:**
- [GitHub Repo](https://github.com/IBM/otter-knowledge)
- [Paper](https://arxiv.org/abs/2306.12802)

**License:**

MIT

**Where to send questions or comments about the dataset:**
- [GitHub Repo](https://github.com/IBM/otter-knowledge)

**Models trained on Otter UBC**
- [ibm/otter_dude_classifier](https://huggingface.co/ibm/otter_dude_classifier)
- [ibm/otter_dude_distmult](https://huggingface.co/ibm/otter_dude_distmult)
- [ibm/otter_dude_transe](https://huggingface.co/ibm/otter_dude_transe)
license:mit arxiv:2306.12802 region:us
{"license": "mit"}
TAGS
#license-mit #arxiv-2306.12802 #region-us 



# Otter DUDe Dataset Card
Otter DUDe includes 1,452,568 instances of drug-target interactions.

## Dataset details
#### DUDe 
DUDe comprises a collection of 22,886 active compounds and their corresponding affinities towards 102 targets. For our study, we utilized a preprocessed version of the DUDe, which includes 1,452,568 instances of drug-target interactions. To prevent any data leakage, we eliminated the negative interactions and the overlapping triples with the TDC DTI dataset. As a result, we were left with a total of 40,216 drug-target interaction pairs.

Original dataset:
- Citation: Samuel Sledzieski, Rohit Singh, Lenore Cowen, and Bonnie Berger. Adapting protein language models for rapid dti prediction. bioRxiv, pages 2022–11, 2022

Paper or resources for more information:
- GitHub Repo
- Paper

License:

MIT

Where to send questions or comments about the dataset:
- GitHub Repo

Models trained on Otter UBC
- ibm/otter_dude_classifier
- ibm/otter_dude_distmult
- ibm/otter_dude_transe
# Otter DUDe Dataset Card
Otter DUDe includes 1,452,568 instances of drug-target interactions. ## Dataset details #### DUDe 
DUDe comprises a collection of 22,886 active compounds and their corresponding affinities towards 102 targets. For our study, we utilized a preprocessed version of the DUDe, which includes 1,452,568 instances of drug-target interactions. To prevent any data leakage, we eliminated the negative interactions and the overlapping triples with the TDC DTI dataset. As a result, we were left with a total of 40,216 drug-target interaction pairs.

Original dataset:
- Citation: Samuel Sledzieski, Rohit Singh, Lenore Cowen, and Bonnie Berger. Adapting protein language models for rapid dti prediction. bioRxiv, pages 2022–11, 2022

Paper or resources for more information:
- GitHub Repo
- Paper

License:

MIT

Where to send questions or comments about the dataset:
- GitHub Repo

Models trained on Otter UBC
- ibm/otter_dude_classifier
- ibm/otter_dude_distmult
- ibm/otter_dude_transe
TAGS
#license-mit #arxiv-2306.12802 #region-us 
 # Otter DUDe Dataset Card
Otter DUDe includes 1,452,568 instances of drug-target interactions. ## Dataset details #### DUDe 
DUDe comprises a collection of 22,886 active compounds and their corresponding affinities towards 102 targets. For our study, we utilized a preprocessed version of the DUDe, which includes 1,452,568 instances of drug-target interactions. To prevent any data leakage, we eliminated the negative interactions and the overlapping triples with the TDC DTI dataset. As a result, we were left with a total of 40,216 drug-target interaction pairs.

Original dataset:
- Citation: Samuel Sledzieski, Rohit Singh, Lenore Cowen, and Bonnie Berger. Adapting protein language models for rapid dti prediction. bioRxiv, pages 2022–11, 2022

Paper or resources for more information:
- GitHub Repo
- Paper

License:

MIT

Where to send questions or comments about the dataset:
- GitHub Repo

Models trained on Otter UBC
- ibm/otter_dude_classifier
- ibm/otter_dude_distmult
- ibm/otter_dude_transe
passage: TAGS
#license-mit #arxiv-2306.12802 #region-us 
# Otter DUDe Dataset Card
Otter DUDe includes 1,452,568 instances of drug-target interactions.## Dataset details#### DUDe 
DUDe comprises a collection of 22,886 active compounds and their corresponding affinities towards 102 targets. For our study, we utilized a preprocessed version of the DUDe, which includes 1,452,568 instances of drug-target interactions. To prevent any data leakage, we eliminated the negative interactions and the overlapping triples with the TDC DTI dataset. As a result, we were left with a total of 40,216 drug-target interaction pairs.

Original dataset:
- Citation: Samuel Sledzieski, Rohit Singh, Lenore Cowen, and Bonnie Berger. Adapting protein language models for rapid dti prediction. bioRxiv, pages 2022–11, 2022

Paper or resources for more information:
- GitHub Repo
- Paper

License:

MIT

Where to send questions or comments about the dataset:
- GitHub Repo

Models trained on Otter UBC
- ibm/otter_dude_classifier
- ibm/otter_dude_distmult
- ibm/otter_dude_transe