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--- |
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language: en |
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source_datasets: curated |
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tags: |
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- chemistry |
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- toxicology |
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pretty_name: Human & Rat Liver Microsomal Stability |
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dataset_summary: >- |
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Curation of databases of compounds for assessing human liver microsomes (HLM) |
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stability and rat liver microsomes (RLM) stability. |
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citation: |- |
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@article{ |
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author = {Longqiang Li, Zhou Lu, Guixia Liu, Yun Tang, and Weihua Li}, |
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doi = {10.1021/acs.chemrestox.2c00207}, |
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journal = {Chemical Research in Toxicology}, |
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number = {9}, |
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title = {In Silico Prediction of Human and Rat Liver Microsomal Stability via Machine Learning Methods}, |
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volume = {35}, |
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year = {2022}, |
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url = {https://pubs.acs.org/doi/10.1021/acs.chemrestox.2c00207}, |
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publisher = {American Chemical Society} |
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} |
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size_categories: |
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- 10K<n<100K |
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config_names: |
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- HLM |
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- RLM |
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- Marketed_Drug |
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configs: |
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- config_name: HLM |
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data_files: |
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- split: test |
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path: HLM/test.csv |
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- split: train |
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path: HLM/train.csv |
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- split: external |
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path: HLM/external.csv |
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- config_name: RLM |
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data_files: |
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- split: test |
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path: RLM/test.csv |
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- split: train |
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path: RLM/train.csv |
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- split: external |
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path: RLM/external.csv |
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- config_name: Marketed_Drug |
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dataset_info: |
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- config_name: HLM |
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features: |
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- name: ID |
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dtype: string |
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- name: SMILES |
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dtype: string |
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- name: 'Y' |
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dtype: int64 |
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description: >- |
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Binary classification where '0' represents 'stable' compounds and '1' |
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represents 'unstable' compounds. |
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splits: |
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- name: train |
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num_bytes: 190968 |
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num_examples: 4771 |
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- name: test |
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num_bytes: 45368 |
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num_examples: 1131 |
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- name: external |
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num_bytes: 4568 |
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num_examples: 111 |
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- config_name: RLM |
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features: |
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- name: ID |
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dtype: string |
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- name: SMILES |
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dtype: string |
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- name: 'Y' |
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dtype: int64 |
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description: >- |
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Binary classification where '0' represents 'stable' compounds and '1' |
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represents 'unstable' compounds. |
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splits: |
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- name: train |
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num_bytes: 100608 |
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num_examples: 2512 |
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- name: test |
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num_bytes: 23968 |
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num_examples: 596 |
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- name: external |
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num_bytes: 99408 |
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num_examples: 2484 |
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- config_name: Marketed_Drug |
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features: |
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- name: SMILES |
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dtype: string |
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- name: Class |
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dtype: int64 |
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description: >- |
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Binary classification where '0' represents 'stable' compounds and '1' |
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represents 'unstable' compounds. |
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- name: Online server predicted class |
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dtype: int64 |
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description: >- |
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Binary classification where '0' represents 'stable' compounds and '1' |
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represents 'unstable' compounds. |
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- name: Our predicted class |
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dtype: int64 |
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description: >- |
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Binary classification where '0' represents 'stable' compounds and '1' |
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represents 'unstable' compounds. |
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task_categories: |
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- tabular-classification |
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--- |
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# Human & Rat Liver Microsomal Stability |
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3345 RLM and 6420 HLM compounds were initially collected from the ChEMBL bioactivity database. |
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(HLM ID: 613373, 2367379, and 612558; RLM ID: 613694, 2367428, and 612558) |
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Finally, the RLM stability data set contains 3108 compounds, and the HLM stability data set contains 5902 compounds. |
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For the RLM stability data set, 1542 (49.6%) compounds were classified as stable, and 1566 (50.4%) compounds were classified as unstable, |
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among which the training and test sets contain 2512 and 596 compounds, respectively. |
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The experimental data from the National Center for Advancing Translational Sciences (PubChem AID 1508591) were used as the RLM external set. |
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For the HLM data set, 3799 (64%) compounds were classified as stable, and 2103 (36%) compounds were classified as unstable. |
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In addition, an external set from Liu et al.12 was used to evaluate the predictive power of the HLM model. |
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The datasets uploaded to our Hugging Face repository are sanitized and reorganized versions. |
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(We have sanitized the molecules from the original paper, using MolVS.) |
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## Quickstart Usage |
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### Load a dataset in python |
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Each subset can be loaded into python using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library. |
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First, from the command line install the `datasets` library |
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$ pip install datasets |
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then, from within python load the datasets library |
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>>> import datasets |
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and load one of the `HLM_RLM` datasets, e.g., |
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>>> HLM = datasets.load_dataset("maomlab/HLM_RLM", name = "HLM") |
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Downloading readme: 100%|████████████████████████| 6.93k/6.93k [00:00<00:00, 280kB/s] |
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Downloading data: 100%|██████████████████████████| 680k/680k [00:00<00:00, 946kB/s] |
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Downloading data: 100%|██████████████████████████| 925k/925k [00:01<00:00, 634kB/s] |
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Downloading data: 100%|██████████████████████████| 39.7k/39.7k [00:00<00:00, 90.8kB/s] |
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Generating test split: 100%|█████████████████████| 1131/1131 [00:00<00:00, 20405.98 examples/s] |
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Generating train split: 100%|████████████████████| 4771/4771 [00:00<00:00, 65495.46 examples/s] |
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Generating external split: 100%|████████████████████| 111/111 [00:00<00:00, 3651.94 examples/s] |
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and inspecting the loaded dataset |
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>>> HLM |
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HLM |
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DatasetDict({ |
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test: Dataset({ |
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features: ['ID','SMILES', 'Y'], |
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num_rows: 1131 |
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}) |
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train: Dataset({ |
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features: ['ID','SMILES', 'Y'], |
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num_rows: 4771 |
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}) |
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external: Dataset({ |
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features: ['ID','SMILES', 'Y'], |
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num_rows: 111 |
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}) |
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}) |
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### Use a dataset to train a model |
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One way to use the dataset is through the [MolFlux](https://exscientia.github.io/molflux/) package developed by Exscientia. |
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First, from the command line, install `MolFlux` library with `catboost` and `rdkit` support |
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pip install 'molflux[catboost,rdkit]' |
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then load, featurize, split, fit, and evaluate the a catboost model |
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import json |
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from datasets import load_dataset |
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from molflux.datasets import featurise_dataset |
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from molflux.features import load_from_dicts as load_representations_from_dicts |
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from molflux.splits import load_from_dict as load_split_from_dict |
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from molflux.modelzoo import load_from_dict as load_model_from_dict |
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from molflux.metrics import load_suite |
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split_dataset = load_dataset('maomlab/HLM_RLM', name = 'HLM') |
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split_featurised_dataset = featurise_dataset( |
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split_dataset, |
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column = "SMILES", |
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representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}])) |
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model = load_model_from_dict({ |
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"name": "cat_boost_classifier", |
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"config": { |
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"x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'], |
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"y_features": ['Y'], |
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}}) |
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model.train(split_featurised_dataset["train"]) |
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preds = model.predict(split_featurised_dataset["test"]) |
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classification_suite = load_suite("classification") |
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scores = classification_suite.compute( |
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references=split_featurised_dataset["test"]['Y'], |
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predictions=preds["cat_boost_classifier::Y"]) |
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## Citation |
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Chem. Res. Toxicol. 2022, 35, 9, 1614–1624 |
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Publication Date:September 2, 2022 |
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https://doi.org/10.1021/acs.chemrestox.2c00207 |
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dataset license: was not specified |
