Omkar2118/datasets huggingface dataset:
KIBA

Dataset Description: Toward making use of the complementary information captured by the various bioactivity types, including IC50, K(i), and K(d), Tang et al. introduces a model-based integration approach, termed KIBA to generate an integrated drug-target bioactivity matrix.

Task Description: Regression. Given the target amino acid sequence/compound SMILES string, predict their binding affinity.

Dataset Statistics: 0.3.2 Update: 117,657 DTI pairs, 2,068 drugs, 229 proteins. Before: 118,036 DTI pairs, 2,068 drugs, 229 proteins.

References:

[1] Tang J, Szwajda A, Shakyawar S, et al. Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis. J Chem Inf Model. 2014;54(3):735-743.

[2] Huang, Kexin, et al. “DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction” Bioinformatics.
DAVIS-DTA

Dataset Description: The interaction of 72 kinase inhibitors with 442 kinases covering >80% of the human catalytic protein kinome.

Task Description: Regression. Given the target amino acid sequence/compound SMILES string, predict their binding affinity.

Dataset Statistics: 0.3.2 Update: 25,772 DTI pairs, 68 drugs, 379 proteins. Before: 27,621 DTI pairs, 68 drugs, 379 proteins.

[1] Davis, M., Hunt, J., Herrgard, S. et al. Comprehensive analysis of kinase inhibitor selectivity. Nat Biotechnol 29, 1046–1051 (2011).

[2] Huang, Kexin, et al. “DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction” Bioinformatics.
SheetCopilot

The SheetCopilot dataset contains 28 evaluation workbooks and 221 spreadsheet manipulation tasks that are applied to these workbooks. These tasks involve diverse atomic actions related to six task categories (i.e. Entry and manipulation, Formatting, Management, Charts, Pivot Table, and Formula).

Dataset statistics:

1. Each task possesses one or more ground truth solutions.

2. The lengths of the task instructions range from 20 to 530 characters, with most tasks between 80 and 110 characters.

3. The number of atomic actions required by each task ranges from 1 to 9.

Evaluation metrics:

1. Execution success rate, pass rate, and the number of used actions are evaluated to judge the functional correctness and efficiency of a method.

2. A submitted solution is considered correct if the properties to be checked match those of any of the GT solutions of the corresponding task.

Please download the full datasets in our Github Repo:

https://github.com/BraveGroup/SheetCopilot

Thanks for using our dataset!