Marissa Dolorfino commited on
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  1. README.md +2 -2
README.md CHANGED
@@ -77,7 +77,7 @@ The following queries were used to retrieve all Protein Data Bank (PDB) structur
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  * HSA query summary: Text search inside all attributes = 'albumin' AND Scientific Name of the Source Organism = "Homo sapiens" AND Number of Distinct Non-polymer Entities >= 1
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  These queries yielded 640, 128, and 1,920 initial structures (some with multiple ligands) for BRD4, sEH, and HSA, respectively. The raw data can be found here:
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- `data/raw/PDB-query_rawdata.csv`
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  ### Data Cleaning
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  This dataset contains some proteins that contain multiple ligands, which each appear as separate rows. In this case, the columns in subsequent rows that refer only to the protein are left empty. To fix this, rows with only entries in the `Ligand ID`, `Ligand Name`, `Ligand SMILES` and `Protein` will have NA values filled with the row from directly above. This is accomplished by the `fill_nans_iteratively` function in `src/utils.py`.
@@ -94,7 +94,7 @@ To clean the data, clone this repository and run (from the parent directory):
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  `python src/clean_data.py`
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- The cleaned data will be output in the `data/processed` directory, and the output of running the above script should look similar to:
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  ```
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  length of data before processing: 20563
 
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  * HSA query summary: Text search inside all attributes = 'albumin' AND Scientific Name of the Source Organism = "Homo sapiens" AND Number of Distinct Non-polymer Entities >= 1
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  These queries yielded 640, 128, and 1,920 initial structures (some with multiple ligands) for BRD4, sEH, and HSA, respectively. The raw data can be found here:
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+ `raw/PDB-query_rawdata.csv`
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  ### Data Cleaning
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  This dataset contains some proteins that contain multiple ligands, which each appear as separate rows. In this case, the columns in subsequent rows that refer only to the protein are left empty. To fix this, rows with only entries in the `Ligand ID`, `Ligand Name`, `Ligand SMILES` and `Protein` will have NA values filled with the row from directly above. This is accomplished by the `fill_nans_iteratively` function in `src/utils.py`.
 
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  `python src/clean_data.py`
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+ The cleaned data will be output in the `processed` directory, and the output of running the above script should look similar to:
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  ```
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  length of data before processing: 20563