SMPrecursorPrediction
Project description
SMPrecursorPredictor
A ML pipeline for the prediction of specialised metabolites starting substances.
Installation
Manually
- Clone the repository and move into the directory:
git clone
cd SMPrecursorPredictor
- Create a conda environment and activate it:
conda create -n sm_precursor_predictor python=3.10
conda activate sm_precursor_predictor
- Install the dependencies:
pip install -r requirements.txt
- Install the package:
pip install .
Pypi
- Create a conda environment and activate it:
conda create -n sm_precursor_predictor python=3.10
conda activate sm_precursor_predictor
pip install SMPrecursorPrediction
Making predictions
Models available:
- Layered FP + Low Variance FS + Ridge Classifier
- Morgan FP + Ridge Classifier
from sm_precursor_predictor import predict_precursors
precursors = predict_precursors(
["[H][C@]89CN(CCc1c([nH]c2ccccc12)[C@@](C(=O)OC)(c3cc4c(cc3OC)N(C)[C@@]5([H])[C@@]"
"(O)(C(=O)OC)[C@H](OC(C)=O)[C@]7(CC)C=CCN6CC[C@]45[C@@]67[H])C8)C[C@](O)(CC)C9",
"COC1=C(C=CC(=C1)C2=C(C(=O)C3=C(C=C(C=C3O2)O)O)O[C@H]4[C@@H]([C@H]([C@H]([C@H](O4)CO)O)O)O)O"],
model="Layered FP + Low Variance FS + Ridge Classifier")
print(precursors)
or
read a csv file with a column of SMILES and a column of IDs and save the predictions in a csv file:
from sm_precursor_predictor import predict_from_csv
predictions = predict_from_csv("path_to_csv",
smiles_field="SMILES",
ids_field="ID",
model="Layered FP + Low Variance FS + Ridge Classifier")
predictions.to_csv("path_to_save_predictions.csv")
Making and explaining predictions
This is only possible with one model: Morgan FP + Ridge Classifier.
Example with linalool:
from sm_precursor_predictor import get_prediction_and_explanation
prediction, images, plots = get_prediction_and_explanation(smiles="CC(=CCCC(C)(C=C)O)C", threshold=0.20)
prediction
['Geranyl diphosphate']
images[0]
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