--- language: en source_datasets: curated license: mit tags: - chemistry - toxicology pretty_name: Human & Rat Liver Microsomal Stability dataset_summary: >- Curation of databases of compounds for assessing human liver microsomes (HLM) stability and rat liver microsomes (RLM) stability. citation: |- @article{ author = {Longqiang Li, Zhou Lu, Guixia Liu, Yun Tang, and Weihua Li}, doi = {10.1021/acs.chemrestox.2c00207}, journal = {Chemical Research in Toxicology}, number = {9}, title = {In Silico Prediction of Human and Rat Liver Microsomal Stability via Machine Learning Methods}, volume = {35}, year = {2022}, url = {https://pubs.acs.org/doi/10.1021/acs.chemrestox.2c00207}, publisher = {American Chemical Society} } size_categories: - 10K- Binary classification where '0' represents 'stable' compounds and '1' represents 'unstable' compounds. splits: - name: train num_bytes: 190968 num_examples: 4771 - name: test num_bytes: 45368 num_examples: 1131 - name: external num_bytes: 4568 num_examples: 111 - config_name: RLM features: - name: ID dtype: string - name: SMILES dtype: string - name: Y dtype: int64 description: >- Binary classification where '0' represents 'stable' compounds and '1' represents 'unstable' compounds. splits: - name: train num_bytes: 100608 num_examples: 2512 - name: test num_bytes: 23968 num_examples: 596 - name: external num_bytes: 99408 num_examples: 2484 - config_name: Marketed_Drug features: - name: SMILES dtype: string - name: Class dtype: int64 description: >- Binary classification where '0' represents 'stable' compounds and '1' represents 'unstable' compounds. - name: Online server predicted class dtype: int64 description: >- Binary classification where '0' represents 'stable' compounds and '1' represents 'unstable' compounds. - name: Our predicted class dtype: int64 description: >- Binary classification where '0' represents 'stable' compounds and '1' represents 'unstable' compounds. task_categories: - tabular-classification --- # Human & Rat Liver Microsomal Stability 3345 RLM and 6420 HLM compounds were initially collected from the ChEMBL bioactivity database. (HLM ID: 613373, 2367379, and 612558; RLM ID: 613694, 2367428, and 612558) Finally, the RLM stability data set contains 3108 compounds, and the HLM stability data set contains 5902 compounds. For the RLM stability data set, 1542 (49.6%) compounds were classified as stable, and 1566 (50.4%) compounds were classified as unstable, among which the training and test sets contain 2512 and 596 compounds, respectively. The experimental data from the National Center for Advancing Translational Sciences (PubChem AID 1508591) were used as the RLM external set. For the HLM data set, 3799 (64%) compounds were classified as stable, and 2103 (36%) compounds were classified as unstable. In addition, an external set from Liu et al.12 was used to evaluate the predictive power of the HLM model. The datasets uploaded to our Hugging Face repository are sanitized and reorganized versions. (We have sanitized the molecules from the original paper, using MolVS.) ## Quickstart Usage ### Load a dataset in python Each subset can be loaded into python using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library. First, from the command line install the `datasets` library $ pip install datasets then, from within python load the datasets library >>> import datasets and load one of the `HLM_RLM` datasets, e.g., >>> HLM = datasets.load_dataset("maomlab/HLM_RLM", name = "HLM") Downloading readme: 100%|████████████████████████| 6.93k/6.93k [00:00<00:00, 280kB/s] Downloading data: 100%|██████████████████████████| 680k/680k [00:00<00:00, 946kB/s] Downloading data: 100%|██████████████████████████| 925k/925k [00:01<00:00, 634kB/s] Downloading data: 100%|██████████████████████████| 39.7k/39.7k [00:00<00:00, 90.8kB/s] Generating test split: 100%|█████████████████████| 1131/1131 [00:00<00:00, 20405.98 examples/s] Generating train split: 100%|████████████████████| 4771/4771 [00:00<00:00, 65495.46 examples/s] Generating external split: 100%|████████████████████| 111/111 [00:00<00:00, 3651.94 examples/s] and inspecting the loaded dataset >>> HLM HLM DatasetDict({ test: Dataset({ features: ['ID','SMILES', 'Y'], num_rows: 1131 }) train: Dataset({ features: ['ID','SMILES', 'Y'], num_rows: 4771 }) external: Dataset({ features: ['ID','SMILES', 'Y'], num_rows: 111 }) }) ### Use a dataset to train a model One way to use the dataset is through the [MolFlux](https://exscientia.github.io/molflux/) package developed by Exscientia. First, from the command line, install `MolFlux` library with `catboost` and `rdkit` support pip install 'molflux[catboost,rdkit]' then load, featurize, split, fit, and evaluate the a catboost model import json from datasets import load_dataset from molflux.datasets import featurise_dataset from molflux.features import load_from_dicts as load_representations_from_dicts from molflux.splits import load_from_dict as load_split_from_dict from molflux.modelzoo import load_from_dict as load_model_from_dict from molflux.metrics import load_suite split_dataset = load_dataset('maomlab/HLM_RLM', name = 'HLM') split_featurised_dataset = featurise_dataset( split_dataset, column = "SMILES", representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}])) model = load_model_from_dict({ "name": "cat_boost_classifier", "config": { "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'], "y_features": ['Y'], }}) model.train(split_featurised_dataset["train"]) preds = model.predict(split_featurised_dataset["test"]) classification_suite = load_suite("classification") scores = classification_suite.compute( references=split_featurised_dataset["test"]['Y'], predictions=preds["cat_boost_classifier::Y"]) ## Citation Chem. Res. Toxicol. 2022, 35, 9, 1614–1624 Publication Date:September 2, 2022 https://doi.org/10.1021/acs.chemrestox.2c00207