--- license: mit language: - en tags: - chemistry - medicinal chemistry pretty_name: AggregatorAdvisor size_categories: - 10K- Many drug-like molecules phase-separate in aqueous solutions causing false-positives in in biochemical assays often used for drug screening. The Aggregator Advisor (https://advisor.docking.org/) is a web-tool hosted by the Shoichet Lab at UCSF and used to assess the risk of a molecules being an aggregetor, may aggregate in biochemical assays based on the chemical similarity to known aggregators, and physical properties. This dataset includes the known aggregator from the Aggregator Advisor 2022/06 release curated from 20 sources. Since aggregation is dependent on the hydrophobicity of the compound, the predicted logP is also computed for each compound. citation: |- @article {Irwin2015, title = {An Aggregation Advisor for Ligand Discovery}, volume = {58}, ISSN = {1520-4804}, url = {http://dx.doi.org/10.1021/acs.jmedchem.5b01105}, DOI = {10.1021/acs.jmedchem.5b01105}, number = {17}, journal = {Journal of Medicinal Chemistry}, publisher = {American Chemical Society (ACS)}, author = {Irwin, John J. and Duan, Da and Torosyan, Hayarpi and Doak, Allison K. and Ziebart, Kristin T. and Sterling, Teague and Tumanian, Gurgen and Shoichet, Brian K.}, year = {2015}, month = aug, pages = {7076–7087} } config_names: - AggregatorAdvisor configs: - config_name: AggregatorAdvisor data_files: - split: test path: AggregatorAdvisor/test.csv - split: train path: AggregatorAdvisor/train.csv dataset_info: - config_name: AggregatorAdvisor features: - name: SMILES dtype: string - name: substance_id dtype: string - name: aggref_index dtype: int64 - name: logP dtype: float64 - name: reference dtype: string splits: - name: train num_bytes: 404768 num_examples: 10116 - name: test num_bytes: 101288 num_examples: 2529 --- # Aggregator Advisor The [Aggregator Advisor](https://advisor.docking.org/) is a web-tool hosted by the Shoichet Lab at UCSF and used to assess the risk of a molecules being an aggregetor, may aggregate in biochemical assays based on the chemical similarity to known aggregators, and physical properties. The most current release (2022/06) contains 12645 known aggregator molecules from 20 sources. The train and test datasets uploaded to our Hugging Face repository have been sanitized and split from the original dataset If you want to try these processes with the original dataset, please follow the instructions in the [Processing Script.py](https://huggingface.co/datasets/maomlab/AggregatorAdvisor/blob/main/Preprocessing%20Script.py) file located in the AggregatorAdvisor. The [raw_data.csv](https://huggingface.co/datasets/maomlab/AggregatorAdvisor/blob/main/raw_data.csv) is the original dataset from the paper, and the files in [AggregatorAdvisor](https://huggingface.co/datasets/maomlab/AggregatorAdvisor/tree/main/AggregatorAdvisor) are the sanitized version files that we made. ## 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 `AggregatorAdvisor` datasets, e.g., >>> AggregatorAdvisor = datasets.load_dataset("maomlab/AggregatorAdvisor", name = "AggregatorAdvisor") Downloading readme: 100%|██████████| 4.70k/4.70k [00:00<00:00, 277kB/s] Downloading data: 100%|██████████| 530k/530k [00:00<00:00, 303kB/s] Downloading data: 100%|██████████| 2.16M/2.16M [00:00<00:00, 12.1MB/s] Generating test split: 100%|██████████| 2529/2529 [00:00<00:00, 29924.07 examples/s] Generating train split: 100%|██████████| 10116/10116 [00:00<00:00, 95081.99 examples/s] and inspecting the loaded dataset >>> AggregatorAdvisor DatasetDict({ test: Dataset({ features: ['new SMILES', 'substance_id', 'aggref_index', 'logP', 'reference'], num_rows: 2529 }) train: Dataset({ features: ['new SMILES', 'substance_id', 'aggref_index', 'logP', 'reference'], num_rows: 10116 }) }) ### 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 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 and evaluate the catboost model split_dataset = load_dataset('maomlab/AggregatorAdvisor', name = 'AggregatorAdvisor') split_featurised_dataset = featurise_dataset( split_dataset, column = "new SMILES", representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}])) model = load_model_from_dict({ "name": "cat_boost_regressor", "config": { "x_features": ['new SMILES::morgan', 'new SMILES::maccs_rdkit'], "y_features": ['logP']}}) model.train(split_featurised_dataset["train"]) preds = model.predict(split_featurised_dataset["test"]) regression_suite = load_suite("regression") scores = regression_suite.compute( references=split_featurised_dataset["test"]['logP'], predictions=preds["cat_boost_regressor::logP"]) ### Data splits Here we have used the `Realistic Split` method described in [(Martin et al., 2018)](https://doi.org/10.1021/acs.jcim.7b00166) to split the AggregatorAdvisor dataset. ## Citation If you use this dataset please cite: An Aggregation Advisor for Ligand Discovery John J. Irwin, Da Duan, Hayarpi Torosyan, Allison K. Doak, Kristin T. Ziebart, Teague Sterling, Gurgen Tumanian, Brian K. Shoichet, J. Med. Chem. 2015, 58, 17, 7076–7087 DOI: https://doi.org/10.1021/acs.jmedchem.5b01105