README.md

---

license: mit
task_categories:
- text-classification
- text2text-generation
- translation
- zero-shot-classification
tags:
- chemistry
- biology
- SMILES
- benchmark
size_categories:
- 10k<n<100k
pretty_name: 'Lo-Hi Benchmark'
configs:
- config_name: hi-drd2-1
  data_files:
  - split: train
    path: "hi/drd2/train_1.csv.gz"
  - split: test
    path: "hi/drd2/test_1.csv.gz"
- config_name: hi-drd2-2
  data_files:
  - split: train
    path: "hi/drd2/train_2.csv.gz"
  - split: test
    path: "hi/drd2/test_2.csv.gz"
- config_name: hi-drd2-3
  data_files:
  - split: train
    path: "hi/drd2/train_3.csv.gz"
  - split: test
    path: "hi/drd2/test_3.csv.gz"
- config_name: hi-hiv-1
  data_files:
  - split: train
    path: "hi/hiv/train_1.csv.gz"
  - split: test
    path: "hi/hiv/test_1.csv.gz"
- config_name: hi-hiv-2
  data_files:
  - split: train
    path: "hi/hiv/train_2.csv.gz"
  - split: test
    path: "hi/hiv/test_2.csv.gz"
- config_name: hi-hiv-3
  data_files:
  - split: train
    path: "hi/hiv/train_3.csv.gz"
  - split: test
    path: "hi/hiv/test_3.csv.gz"
- config_name: hi-kdr-1
  data_files:
  - split: train
    path: "hi/kdr/train_1.csv.gz"
  - split: test
    path: "hi/kdr/test_1.csv.gz"
- config_name: hi-kdr-2
  data_files:
  - split: train
    path: "hi/kdr/train_2.csv.gz"
  - split: test
    path: "hi/kdr/test_2.csv.gz"
- config_name: hi-kdr-3
  data_files:
  - split: train
    path: "hi/kdr/train_3.csv.gz"
  - split: test
    path: "hi/kdr/test_3.csv.gz"
- config_name: hi-sol-1
  data_files:
  - split: train
    path: "hi/sol/train_1.csv.gz"
  - split: test
    path: "hi/sol/test_1.csv.gz"
- config_name: hi-sol-2
  data_files:
  - split: train
    path: "hi/sol/train_2.csv.gz"
  - split: test
    path: "hi/sol/test_2.csv.gz"
- config_name: hi-sol-3
  data_files:
  - split: train
    path: "hi/sol/train_3.csv.gz"
  - split: test
    path: "hi/sol/test_3.csv.gz"
- config_name: lo-drd2-1
  data_files:
  - split: train
    path: "lo/drd2/train_1.csv.gz"
  - split: test
    path: "lo/drd2/test_1.csv.gz"
- config_name: lo-drd2-2
  data_files:
  - split: train
    path: "lo/drd2/train_2.csv.gz"
  - split: test
    path: "lo/drd2/test_2.csv.gz"
- config_name: lo-drd2-3
  data_files:
  - split: train
    path: "lo/drd2/train_3.csv.gz"
  - split: test
    path: "lo/drd2/test_3.csv.gz"
- config_name: lo-kcnh2-1
  data_files:
  - split: train
    path: "lo/kcnh2/train_1.csv.gz"
  - split: test
    path: "lo/kcnh2/test_1.csv.gz"
- config_name: lo-kcnh2-2
  data_files:
  - split: train
    path: "lo/kcnh2/train_2.csv.gz"
  - split: test
    path: "lo/kcnh2/test_2.csv.gz"
- config_name: lo-kcnh2-3
  data_files:
  - split: train
    path: "lo/kcnh2/train_3.csv.gz"
  - split: test
    path: "lo/kcnh2/test_3.csv.gz"
- config_name: lo-kdr-1
  data_files:
  - split: train
    path: "lo/kdr/train_1.csv.gz"
  - split: test
    path: "lo/kdr/test_1.csv.gz"
- config_name: lo-kdr-2
  data_files:
  - split: train
    path: "lo/kdr/train_2.csv.gz"
  - split: test
    path: "lo/kdr/test_2.csv.gz"
- config_name: lo-kdr-3
  data_files:
  - split: train
    path: "lo/kdr/train_3.csv.gz"
  - split: test
    path: "lo/kdr/test_3.csv.gz"
---

# Lo-Hi Benchmark

Data from [Simon Steshin, Lo-Hi: Practical ML Drug Discovery Benchmark](https://arxiv.org/abs/2310.06399), available from the [GitHub repositiory](https://github.com/SteshinSS/lohi_neurips2023). We used [schemist](https://github.com/scbirlab/schemist) (which in turn uses RDKit)
to add molecuar weight, Murcko scaffold, Crippen cLogP, and topological surface area.

## Dataset Details

From the [original README](https://github.com/SteshinSS/lohi_neurips2023?tab=readme-ov-file):

### Hit Identification
The goal of the Hit Identification task is to find novel molecules that have desirable property, but are dissimilar from the molecules with known activity. There are four datasets simulating this scenario: `DRD2-Hi`, `HIV-Hi`, `KDR-Hi` and `Sol-Hi`. They are binary classification tasks such that the most similar molecules between train and test have ECFP4 Tanimoto similarity < 0.4.

- `data/hi/drd2` -- for DRD2-Hi
- `data/hi/hiv` -- for HIV-Hi
- `data/hi/kdr` -- for KDR-Hi
- `data/hi/sol` -- for Sol-Hi

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: PR AUC.

### Lead Optimization
The goal of the Lead Optimization task is to predict how minor modifications of a molecule affect its activity. There are three datasets simulating this scenario: `DRD2-Lo`, `KCNH2-Lo` and more challenging `KDR-Lo`. They are ranking tasks that have clusters in the test set, so that the molecules in each clusters are quite similar with Tanimoto similarity > 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
<!-- - **Funded by:** The Francis Crick Institute -->
- **License:** MIT

### Dataset Sources [optional]

<!-- Provide the basic links for the dataset. -->

- **Repository:** https://github.com/SteshinSS/lohi_neurips2023

- **Paper:** https://arxiv.org/abs/2310.06399

<!-- - **Demo [optional]:** [More Information Needed] -->



## Uses



Bechmarking chemical property prediction models.



<!-- ### Direct Use   -->



<!-- This section describes suitable use cases for the dataset. -->



<!-- [More Information Needed]



### Out-of-Scope Use



<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->



<!-- [More Information Needed]  -->



## 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

<!-- ## Bias, Risks, and Limitations  -->

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

<!--  [More Information Needed]  -->

<!-- ### Recommendations  -->

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

<!-- Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.  -->

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**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}, 

}

```

<!-- **APA:** -->

<!-- ## Glossary [optional]  -->

<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

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<!-- ## More Information [optional]

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<!-- ## Dataset Card Authors [optional]

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## Dataset Card Contact

[@eachanjohnson](https://huggingface.co/eachanjohnson)