This is the implementation of the paper - Generative Recurrent Networks for De Novo Drug Design
- Now support tensorflow >= 2.5.0
- Changed the code to use tensorflow 2.1.0 (tf.keras)
- Reimplimented all code to use tensorflow 2.0.0 (tf.keras)
- Changed data_loader to use generator to reduce memory usage
- Removed some unused atoms and symbols
- Changed directory layout
This model is built using Python 3.7. See Pipfile or requirements.txt for dependencies.
I strongly recommend using GPU version of tensorflow.Learning this model with all the data is very slow in CPU mode (about 9 hrs / epoch).
RDKit and matplotlib are used for SMILES cleanup, validation, and visualization of molecules and their properties.
Recently, RDKit can be installed with pip, you don't have to use Anaconda!
Scikit-learn is used for PCA.
Just run below. However, all the data is used according to the default setting. So please be careful, it will take a long time.
If you don't have enough time, set data_length to a different value in base_config.json.
$ python train.pyAfter training, experiments/{exp_name}/{YYYY-mm-dd}/config.json is generated.
It's a copy of base_config.json with additional settings for internal varibale. Since it is used for generation, be careful when rewriting.
See example_Randomly_generate_SMILES.ipynb.
See example_Fine-tuning_for_TRPM8.ipynb.
See base_config.json. If you want to change, please edit this file before training.
| parameters | meaning |
|---|---|
| exp_name | experiment name (default: LSTM_Chem) |
| data_filename | filepath for training the model (SMILES file with newline as delimiter) |
| data_length | number of SMILES for training. If you set 0, all the data is used (default: 0) |
| units | size of hidden state vector of two LSTM layers (default: 256, see the paper) |
| num_epochs | number of epochs (default: 22, see the paper) |
| optimizer | optimizer (default: adam) |
| seed | random seed (default: 71) |
| batch_size | batch size (default: 256) |
| validation_split | split ratio for validation (default: 0.10) |
| varbose_training | verbosity mode (default: True) |
| checkpoint_monitor | quantity to monitor (default: val_loss) |
| checkpoint_mode | one of {auto, min, max} (default: min) |
| checkpoint_save_best_only | the latest best model according to the quantity monitored will not be overwritten (default: False) |
| checkpoint_save_weights_only | If True, then only the model's weights will be saved (default: True) |
| checkpoint_verbose | verbosity mode while ModelCheckpoint (default: 1) |
| tensorboard_write_graph | whether to visualize the graph in TensorBoard (defalut: True) |
| sampling_temp | sampling temperature (default: 0.75, see the paper) |
| smiles_max_length | maximum size of generated SMILES (symbol) length (default: 128) |
| finetune_epochs | epochs for fine-tuning (default: 12, see the paper) |
| finetune_batch_size | batch size of finetune (default: 1) |
| finetune_filename | filepath for fine-tune the model (SMILES file with newline as delimiter) |
Download SQLite dump for ChEMBL25 (ftp://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/latest/chembl_25_sqlite.tar.gz),
which is 3.3 GB compressed, and 16 GB uncompressed.
Unpack it the usual way, cd into the directory, and open the database using sqlite console.
$ sqlite3 chembl_25.db
SQLite version 3.30.1 2019-10-10 20:19:45
Enter ".help" for usage hints.
sqlite> .output dataset.smiYou can get SMILES that annotated nM activities according to the following SQL query.
SELECT
DISTINCT canonical_smiles
FROM
compound_structures
WHERE
molregno IN (
SELECT
DISTINCT molregno
FROM
activities
WHERE
standard_type IN ("Kd", "Ki", "Kb", "IC50", "EC50")
AND standard_units = "nM"
AND standard_value < 1000
AND standard_relation IN ("<", "<<", "<=", "=")
INTERSECT
SELECT
molregno
FROM
molecule_dictionary
WHERE
molecule_type = "Small molecule"
);
You can get 556134 SMILES in dataset.smi. According to the paper,
the dataset was preprocessed and duplicates, salts, and stereochemical information were removed,
SMILES strings with lengths from 34 to 74 (tokens). So I made SMILES clean up script.
Run the following to get cleansed SMILES. It takes about 10 miniutes or more. Please wait.
$ python cleanup_smiles.py datasets/dataset.smi datasets/dataset_cleansed.smiYou can get 438552 SMILES. This dataset is used for training.
The paper shows 5 TRPM8 antagonists for fine-tuning.
FC(F)(F)c1ccccc1-c1cc(C(F)(F)F)c2[nH]c(C3=NOC4(CCCCC4)C3)nc2c1
O=C(Nc1ccc(OC(F)(F)F)cc1)N1CCC2(CC1)CC(O)c1cccc(Cl)c1O2
O=C(O)c1ccc(S(=O)(=O)N(Cc2ccc(C(F)(F)C3CC3)c(F)c2)c2ncc3ccccc3c2C2CC2)cc1
Cc1cccc(COc2ccccc2C(=O)N(CCCN)Cc2cccs2)c1
CC(c1ccc(F)cc1F)N(Cc1cccc(C(=O)O)c1)C(=O)c1cc2ccccc2cn1You can see this in datasets/TRPM8_inhibitors_for_fine-tune.smi.
Open the database using sqlite console.
$ sqlite3 chembl_25.db
SQLite version 3.30.1 2019-10-10 20:19:45
Enter ".help" for usage hints.
sqlite> .output known-TRPM8-inhibitors.smiThen issue the following SQL query. I set maximum IC50 activity to 10 uM.
SELECT
DISTINCT canonical_smiles
FROM
activities,
compound_structures
WHERE
assay_id IN (
SELECT
assay_id
FROM
assays
WHERE
tid IN (
SELECT
tid
FROM
target_dictionary
WHERE
pref_name = "Transient receptor potential cation channel subfamily M member 8"
)
)
AND standard_type = "IC50"
AND standard_units = "nM"
AND standard_value < 10000
AND standard_relation IN ("<", "<<", "<=", "=")
AND activities.molregno = compound_structures.molregno;You can get 494 known TRPM8 inhibitors. As described above, clean up the TRPM8 inhibitor SMILES.
Please use the -ft option to ignore SMILES strings (tokens) length restriction.
$ python cleanup_smiles.py -ft datasets/known-TRPM8-inhibitors.smi datasets/known_TRPM8-inhibitors_cleansed.smiYou can get 477 SMILES. I used this for mere visualization of the results of fine-tuning.
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