- RAscore is a score learned from the predictions of a computer aided synthesis planning tool (AiZynthfinder: https://github.com/MolecularAI/aizynthfinder).
- RAscore is intended to be a binary score, indicating whether the underlying computer aided synthesis planning tool can find a route (1) or not (0) to a given compound.
- The tool has been trained on 200,000 compounds from ChEMBL and so is limited to compounds within similar regions of chemical space. It is intended to predict the retrosyntehtic accessibility of bioactive molecules.
- Attempts to use the score on more exotic compounds such as those found in the GDB databases will not work:
- In this case the model will need to be switched to
GDBscore, the corresponding models can be found in the models folder or downloaded from the pre-print server.
- In this case the model will need to be switched to
Follow the steps in the defined order to avoid conflicts.
- Create an environment (note: the python version must be >=3.7):
conda create --name myenv python=3.7
or use an existing environment
- Install rdkit 2020.03, tensorflow 2, and XGboost (if already installed skip this step)
conda install -c rdkit rdkit -y
conda install -c anaconda tensorflow>=2.1.0 -y
pip install -U scikit-learn
pip install xgboost tqdm
- Clone the RAscore repository
git clone https://github.com/reymond-group/RAscore.git
change directory to the repository
cd RAscore
-
Install RAscore
python -m pip install -e . -
Unzip models into models folder. A zip file containing the models is also available on the pre-print server.
cd RAscore/model/
unzip models
If you want to retrain models, or train your own models using the hyperparameter optimisation framework found in the 'model_building' folder, then the following should be installed in the environemnt also:
pip install -r requirements.txt
The SYBA, SCscore and SAscore should also be downloaded for descriptor calculations and training scripts modified to reflect the locations of the models:
- https://github.com/lich-uct/syba
- https://github.com/connorcoley/scscore
- https://github.com/rdkit/rdkit/tree/master/Contrib/SA_Score
Depending on if you would like to use the XGB based or Tensorflow based models you can import different modules.
The RAscore models are contained in the following folders:
- RAscore
- DNN_chembl_fcfp_counts
- XGB_chembl_ecfp_counts
- GDBscore
- DNN_gdbchembl_fcfp_counts
- XGB_gdbchembl_ecfp_counts
from RAscore import RAscore_NN #For tensorflow and keras based models
from RAscore import RAscore_XGB #For XGB based models
nn_scorer = RAscore_NN.RAScorerNN('<path-to-repo>/RAscore/RAscore/model/models/DNN_chembl_fcfp_counts/model.h5')
xgb_scorer = RAscore_XGB.RAScorerXGB('<path-to-repo>/RAscore/RAscore/model/models/XGB_chembl_ecfp_counts/model.pkl')
#Imatinib mesylate
imatinib_mesylate = 'CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC=CC(=N4)C5=CN=CC=C5.CS(=O)(=O)O'
nn_scorer.predict(imatinib_mesylate)
0.99522984
xgb_scorer.predict(imatinib_mesylate)
0.99259007
#Omeprazole
omeprazole = 'CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC'
nn_scorer.predict(omeprazole)
0.99999106
xgb_scorer.predict(omeprazole)
0.9556329
#Morphine - Illustrates problem synthesis planning tools face with complex ring systems
morphine = 'CN1CC[C@]23c4c5ccc(O)c4O[C@H]2[C@@H](O)C=C[C@H]3[C@H]1C5'
nn_scorer.predict(morphine)
8.316945e-07
xgb_scorer.predict(morphine)
0.0028359715
A command line interface is provided which gives the flexibility of specifying models.
The interface can be found at:\
RAscore/command_line_interface.py
Usage: command_line_interface.py [OPTIONS]
Options:
-f, --file_path TEXT Absolute path to input file containing one SMILES
on each line. The column should be labelled
"SMILES" or if another header is used, specify it
as an option
-c, --column_header TEXT The name given to the singular column in the file
which contains the SMILES. The column must be
named.
-o, --output_path TEXT Output file path
-m, --model_path TEXT Absolute path to the model to use, if .h5 file
neural network in tensorflow/keras, if .pkl then
XGBoost
--help Show this message and exit.
python command_line_interface.py -f <path-to-smiles-file> -c SMILES -o <path-to-output-file> -m <path-to-model-file>
- Test set contains ca. 20,000 compounds from ChEMBL
- The model was able to separate clusters of solved/unsolved compounds as found by computing the average linkage
- RAscore can better differentiate between solved/unsolved compounds than existing methods.