--- license: mit task_categories: - text-classification - text2text-generation - translation - zero-shot-classification tags: - chemistry - biology - SMILES - benchmark size_categories: - 10k 0.4 to the central molecules, and each cluster has one similar molecule in the train set, representing known hit. - `data/lo/drd2` -- for DRD2-Lo - `data/lo/kcnh2` -- for KCNH2-Lo - `data/lo/kdr` -- for KDR-Lo There are three splits of the datasets. Use only the first split for the hyperparameter tuning. Train your model with the same hyperparameters for all the three splits and calculate mean metric. Metric: spearman correlation is calculated for each cluster in the test set and the mean is taken. ### Dataset Description - **Curated by:** Simon Steshin - **License:** MIT ### Dataset Sources [optional] - **Repository:** https://github.com/SteshinSS/lohi_neurips2023 - **Paper:** https://arxiv.org/abs/2310.06399 ## Uses Bechmarking chemical property prediction models. ## Dataset Structure The data are divided into the Hit Identification (`hi`, binary classification) and Lead Optimization (`lo`, regression) tasks. Within each are several datasets from a number of assays. Within each of these are three splits of train and test. Each split is in a separate pair of train and test files. So the files for split 1 are in `train_1.csv.gz`, `test_1.csv.gz` and the files for split 2 are in `train_2.csv.gz`, `test_2.csv.gz`. ``` . ├── hi │ ├── drd2 │ │ ├── test_1.csv.gz │ │ ├── test_2.csv.gz │ │ ├── test_3.csv.gz │ │ ├── train_1.csv.gz │ │ ├── train_2.csv.gz │ │ └── train_3.csv.gz │ ├── hiv │ │ ├── test_1.csv.gz │ │ ├── test_2.csv.gz │ │ ├── test_3.csv.gz │ │ ├── train_1.csv.gz │ │ ├── train_2.csv.gz │ │ └── train_3.csv.gz │ ├── kdr │ │ ├── test_1.csv.gz │ │ ├── test_2.csv.gz │ │ ├── test_3.csv.gz │ │ ├── train_1.csv.gz │ │ ├── train_2.csv.gz │ │ └── train_3.csv.gz │ └── sol │ ├── test_1.csv.gz │ ├── test_2.csv.gz │ ├── test_3.csv.gz │ ├── train_1.csv.gz │ ├── train_2.csv.gz │ └── train_3.csv.gz └── lo ├── drd2 │ ├── test_1.csv.gz │ ├── test_2.csv.gz │ ├── test_3.csv.gz │ ├── train_1.csv.gz │ ├── train_2.csv.gz │ └── train_3.csv.gz ├── kcnh2 │ ├── test_1.csv.gz │ ├── test_2.csv.gz │ ├── test_3.csv.gz │ ├── train_1.csv.gz │ ├── train_2.csv.gz │ └── train_3.csv.gz └── kdr ├── test_1.csv.gz ├── test_2.csv.gz ├── test_3.csv.gz ├── train_1.csv.gz ├── train_2.csv.gz └── train_3.csv.gz ``` The column headings of the data are: - **smiles**: SMILES string - **value**: The assay result. This is True/False for `hi` and numeric for `lo`. - **id**: Numeric structure identifier - **inchikey**: Unique structure identifier - **scaffold**: Murcko scaffold - **mwt**: Molecular weight - **clogp**: Crippen LogP - **tpsa**: Calculated topological polar surface area. The `hi` datasets also have a `cluster` column, indicating the structural cluster of the compound. ## Dataset Creation ### Curation Rationale To make the Lo-Hi Benchmark readily available with light preprocessing. #### Data Collection and Processing Additional properties were calculated using [schemist](https://github.com/scbirlab/schemist), a tool for processing chemical datasets. #### Who are the source data producers? Simon Steshin (https://github.com/SteshinSS). #### Personal and Sensitive Information None ## Citation **BibTeX:** ``` @misc{steshin2023lohipracticalmldrug, title={Lo-Hi: Practical ML Drug Discovery Benchmark}, author={Simon Steshin}, year={2023}, eprint={2310.06399}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2310.06399}, } ``` ## Dataset Card Contact [@eachanjohnson](https://huggingface.co/eachanjohnson)