---
license: mit
language:
- en
tags:
- chemistry
- medicinal chemistry
pretty_name: AggregatorAdvisor
size_categories:
- 10K<n<100K
dataset_summary: >-
  AggregatorAdvisor identifies molecules that are known to aggregate or may aggregate in biochemical assays.
  The approach is based on the chemical similarity to known aggregators, and physical properties.
  The AggregatorAdvisor dataset contains 12645 compounds from 20 different sources.
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: "new 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 


## 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 `HematoxLong2023` 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"])    

## Citation