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README.md
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AggregatorAdvisor identifies molecules that are known to aggregate or may aggregate in biochemical assays.
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The approach is based on the chemical similarity to known aggregators, and physical properties.
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citation: >-
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@article
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{Irwin2015, title = {An Aggregation Advisor for Ligand Discovery},
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>>> import datasets
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and load one of the `
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>>> AggregatorAdvisor = datasets.load_dataset("maomlab/AggregatorAdvisor", name = "AggregatorAdvisor")
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Downloading readme: 100%|██████████| 4.70k/4.70k [00:00<00:00, 277kB/s]
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predictions=preds["cat_boost_regressor::logP"])
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## Citation
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dataset_summary: >-
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AggregatorAdvisor identifies molecules that are known to aggregate or may aggregate in biochemical assays.
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The approach is based on the chemical similarity to known aggregators, and physical properties.
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The train and test datasets uploaded to our Hugging Face repository have been sanitized and split from the original dataset, which contains 12645 compounds.
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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.
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citation: >-
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@article
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{Irwin2015, title = {An Aggregation Advisor for Ligand Discovery},
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>>> import datasets
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and load one of the `AggregatorAdvisor` datasets, e.g.,
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>>> AggregatorAdvisor = datasets.load_dataset("maomlab/AggregatorAdvisor", name = "AggregatorAdvisor")
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Downloading readme: 100%|██████████| 4.70k/4.70k [00:00<00:00, 277kB/s]
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predictions=preds["cat_boost_regressor::logP"])
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## Citation
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J. Med. Chem. 2015, 58, 17, 7076–7087
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Publication Date:August 21, 2015
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https://doi.org/10.1021/acs.jmedchem.5b01105
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