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---
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 train and test datasets uploaded to our Hugging Face repository have been sanitized and split from the original dataset, which contains 12645 compounds.
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.
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 `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"])
## Citation
J. Med. Chem. 2015, 58, 17, 7076–7087
Publication Date:August 21, 2015
https://doi.org/10.1021/acs.jmedchem.5b01105
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