This repository contains message passing neural networks for molecular property prediction as described in the paper Analyzing Learned Molecular Representations for Property Prediction and as used in the paper A Deep Learning Approach to Antibiotic Discovery.
Documentation: Full documentation of Chemprop is available at https://chemprop.readthedocs.io/en/latest/.
Website: A web prediction interface with some trained Chemprop models is available at chemprop.csail.mit.edu.
Tutorial: These slides provide a Chemprop tutorial and highlight recent additions as of April 28th, 2020.
Please see aicures.mit.edu and the associated data GitHub repo for information about our recent efforts to use Chemprop to identify drug candidates for treating COVID-19.
- Requirements
- Installation
- Web Interface
- Data
- Training
- Predicting
- Encode Fingerprint Latent Representation
- Interpreting Model Prediction
- TensorBoard
- Results
For small datasets (~1000 molecules), it is possible to train models within a few minutes on a standard laptop with CPUs only. However, for larger datasets and larger Chemprop models, we recommend using a GPU for significantly faster training.
To use chemprop with GPUs, you will need:
- cuda >= 8.0
- cuDNN
Chemprop can either be installed from PyPi via pip or from source (i.e., directly from this git repo). The PyPi version includes a vast majority of Chemprop functionality, but some functionality is only accessible when installed from source.
Both options require conda, so first install Miniconda from https://conda.io/miniconda.html.
Then proceed to either option below to complete the installation. Note that on machines with GPUs, you may need to manually install a GPU-enabled version of PyTorch by following the instructions here.
conda create -n chemprop python=3.8conda activate chempropconda install -c conda-forge rdkitpip install git+https://github.com/bp-kelley/descriptastoruspip install chemprop
git clone https://github.com/chemprop/chemprop.gitcd chempropconda env create -f environment.ymlconda activate chemproppip install -e .
Chemprop can also be installed with Docker. Docker makes it possible to isolate the Chemprop code and environment. To install and run our code in a Docker container, follow these steps:
git clone https://github.com/chemprop/chemprop.gitcd chemprop- Install Docker from https://docs.docker.com/install/
docker build -t chemprop .docker run -it chemprop:latest
Note that you will need to run the latter command with nvidia-docker if you are on a GPU machine in order to be able to access the GPUs.
Alternatively, with Docker 19.03+, you can specify the --gpus command line option instead.
In addition, you will also need to ensure that the CUDA toolkit version in the Docker image is compatible with the CUDA driver on your host machine.
Newer CUDA driver versions are backward-compatible with older CUDA toolkit versions.
To set a specific CUDA toolkit version, add cudatoolkit=X.Y to environment.yml before building the Docker image.
For those less familiar with the command line, Chemprop also includes a web interface which allows for basic training and predicting. An example of the website (in demo mode with training disabled) is available here: chemprop.csail.mit.edu.
You can start the web interface on your local machine in two ways. Flask is used for development mode while gunicorn is used for production mode.
Run chemprop_web (or optionally python web.py if installed from source) and then navigate to localhost:5000 in a web browser.
Gunicorn is only available for a UNIX environment, meaning it will not work on Windows. It is not installed by default with the rest of Chemprop, so first run:
pip install gunicorn
Next, navigate to chemprop/web and run gunicorn --bind {host}:{port} 'wsgi:build_app()'. This will start the site in production mode.
- To run this server in the background, add the
--daemonflag. - Arguments including
init_dbanddemocan be passed with this pattern:'wsgi:build_app(init_db=True, demo=True)' - Gunicorn documentation can be found here.
In order to train a model, you must provide training data containing molecules (as SMILES strings) and known target values. Targets can either be real numbers, if performing regression, or binary (i.e. 0s and 1s), if performing classification. Target values which are unknown can be left as blanks.
Our model can either train on a single target ("single tasking") or on multiple targets simultaneously ("multi-tasking").
The data file must be be a CSV file with a header row. For example:
smiles,NR-AR,NR-AR-LBD,NR-AhR,NR-Aromatase,NR-ER,NR-ER-LBD,NR-PPAR-gamma,SR-ARE,SR-ATAD5,SR-HSE,SR-MMP,SR-p53
CCOc1ccc2nc(S(N)(=O)=O)sc2c1,0,0,1,,,0,0,1,0,0,0,0
CCN1C(=O)NC(c2ccccc2)C1=O,0,0,0,0,0,0,0,,0,,0,0
...
By default, it is assumed that the SMILES are in the first column (can be changed using --number_of_molecules) and the targets are in the remaining columns. However, the specific columns containing the SMILES and targets can be specified using the --smiles_columns <column_1> ... and --target_columns <column_1> <column_2> ... flags, respectively.
Datasets from MoleculeNet and a 450K subset of ChEMBL from http://www.bioinf.jku.at/research/lsc/index.html have been preprocessed and are available in data.tar.gz. To uncompress them, run tar xvzf data.tar.gz.
To train a model, run:
chemprop_train --data_path <path> --dataset_type <type> --save_dir <dir>
where <path> is the path to a CSV file containing a dataset, <type> is either "classification" or "regression" depending on the type of the dataset, and <dir> is the directory where model checkpoints will be saved.
For example:
chemprop_train --data_path data/tox21.csv --dataset_type classification --save_dir tox21_checkpoints
A full list of available command-line arguments can be found in chemprop/args.py.
If installed from source, chemprop_train can be replaced with python train.py.
Notes:
- The default metric for classification is AUC and the default metric for regression is RMSE. Other metrics may be specified with
--metric <metric>. --save_dirmay be left out if you don't want to save model checkpoints.--quietcan be added to reduce the amount of debugging information printed to the console. Both a quiet and verbose version of the logs are saved in thesave_dir.
Our code supports several methods of splitting data into train, validation, and test sets.
Random: By default, the data will be split randomly into train, validation, and test sets.
Scaffold: Alternatively, the data can be split by molecular scaffold so that the same scaffold never appears in more than one split. This can be specified by adding --split_type scaffold_balanced.
Separate val/test: If you have separate data files you would like to use as the validation or test set, you can specify them with --separate_val_path <val_path> and/or --separate_test_path <test_path>. If both are provided, then the data specified by --data_path is used entirely as the training data. If only one separate path is provided, the --data_path data is split between train data and either val or test data, whichever is not provided separately.
Note: By default, both random and scaffold split the data into 80% train, 10% validation, and 10% test. This can be changed with --split_sizes <train_frac> <val_frac> <test_frac>. For example, the default setting is --split_sizes 0.8 0.1 0.1. Both also involve a random component and can be seeded with --seed <seed>. The default setting is --seed 0.
k-fold cross-validation can be run by specifying --num_folds <k>. The default is --num_folds 1.
To train an ensemble, specify the number of models in the ensemble with --ensemble_size <n>. The default is --ensemble_size 1.
Although the default message passing architecture works quite well on a variety of datasets, optimizing the hyperparameters for a particular dataset often leads to marked improvement in predictive performance. We have automated hyperparameter optimization via Bayesian optimization (using the hyperopt package), which will find the optimal hidden size, depth, dropout, and number of feed-forward layers for our model. Optimization can be run as follows:
chemprop_hyperopt --data_path <data_path> --dataset_type <type> --num_iters <n> --config_save_path <config_path>
where <n> is the number of hyperparameter settings to try and <config_path> is the path to a .json file where the optimal hyperparameters will be saved.
If installed from source, chemprop_hyperopt can be replaced with python hyperparameter_optimization.py.
Once hyperparameter optimization is complete, the optimal hyperparameters can be applied during training by specifying the config path as follows:
chemprop_train --data_path <data_path> --dataset_type <type> --config_path <config_path>
Note that the hyperparameter optimization script sees all the data given to it. The intended use is to run the hyperparameter optimization script on a dataset with the eventual test set held out. If you need to optimize hyperparameters separately for several different cross validation splits, you should e.g. set up a bash script to run hyperparameter_optimization.py separately on each split's training and validation data with test held out.
By default, the atom-level representations from the message passing network are averaged over all atoms of a molecule to yield a molecule-level representation. Alternatively, the atomic vectors can be summed up (by specifying --aggregation sum) or summed up and divided by a constant number N (by specifying --aggregation norm --aggregation_norm <N>). A reasonable value for N is usually the average number of atoms per molecule in the dataset of interest. The default is --aggregation_norm 100.
While the model works very well on its own, especially after hyperparameter optimization, we have seen that additional features can further improve performance on certain datasets. The additional features can be added at the atom-, bond, or molecule-level. Molecule-level features can be either automatically generated by RDKit or custom features provided by the user.
As a starting point, we recommend using pre-normalized RDKit features by using the --features_generator rdkit_2d_normalized --no_features_scaling flags. In general, we recommend NOT using the --no_features_scaling flag (i.e. allow the code to automatically perform feature scaling), but in the case of rdkit_2d_normalized, those features have been pre-normalized and don't require further scaling.
The full list of available features for --features_generator is as follows.
morgan is binary Morgan fingerprints, radius 2 and 2048 bits.
morgan_count is count-based Morgan, radius 2 and 2048 bits.
rdkit_2d is an unnormalized version of 200 assorted rdkit descriptors. Full list can be found at the bottom of our paper: https://arxiv.org/pdf/1904.01561.pdf
rdkit_2d_normalized is the CDF-normalized version of the 200 rdkit descriptors.
If you install from source, you can modify the code to load custom features as follows:
- Generate features: If you want to generate features in code, you can write a custom features generator function in
chemprop/features/features_generators.py. Scroll down to the bottom of that file to see a features generator code template. - Load features: If you have features saved as a numpy
.npyfile or as a.csvfile, you can load the features by using--features_path /path/to/features. Note that the features must be in the same order as the SMILES strings in your data file. Also note that.csvfiles must have a header row and the features should be comma-separated with one line per molecule. By default, provided features will be normalized unless the flag--no_features_scalingis used.
Similar to the additional molecular features described above, you can also provide additional atomic features via --atom_descriptors_path /path/to/features with valid file formats:
.npzfile, where descriptors are saved as 2D array for each molecule in the exact same order as the SMILES strings in your data file..pkl/.pckl/.picklecontaining a pandas dataframe with smiles as index and a numpy array of descriptors as columns..sdfcontaining all mol blocks with descriptors as entries.
The order of the descriptors for each atom per molecule must match the ordering of atoms in the RDKit molecule object. Further information on supplying atomic descriptors can be found here.
Users must select in which way atom descriptors are used. The command line option --atom_descriptors descriptor concatenates the new features to the embedded atomic features after the D-MPNN with an additional linear layer. The option --atom_descriptors feature concatenates the features to each atomic feature vector before the D-MPNN, so that they are used during message-passing. Alternatively, the user can overwrite the default atom features with the custom features using the option --overwrite_default_atom_features.
Similar to the molecule-level features, the atom-level descriptors and features are scaled by default. This can be disabled with the option --no_atom_descriptor_scaling
Bond-level features can be provided in the same format as the atom-level features, using the option --bond_features_path /path/to/features. The order of the features for each molecule must match the bond ordering in the RDKit molecule object.
The bond-level features are concatenated with the bond feature vectors before the D-MPNN, such that they are used during message-passing. Alternatively, the user can overwrite the default bond features with the custom features using the option --overwrite_default_bond_features.
Similar to molecule-, and atom-level features, the bond-level features are scaled by default. This can be disabled with the option --no_bond_features_scaling.
As an alternative to molecule SMILES, Chemprop can also process atom-mapped reaction SMILES (see Daylight manual for details on reaction SMILES), which consist of three parts denoting reactants, agents and products, separated by ">". Use the option --reaction to enable the input of reactions, which transforms the reactants and products of each reaction to the corresponding condensed graph of reaction and changes the initial atom and bond features to hold information from both the reactant and product (option --reaction_mode reac_prod), or from the reactant and the difference upon reaction (option --reaction_mode reac_diff, default) or from the product and the difference upon reaction (option --reaction_mode prod_diff). In reaction mode, Chemprop thus concatenates information to each atomic and bond feature vector, for example, with option --reaction_mode reac_prod, each atomic feature vector holds information on the state of the atom in the reactant (similar to default Chemprop), and concatenates information on the state of the atom in the product, so that the size of the D-MPNN increases slightly. Agents are discarded. Functions incompatible with a reaction as input (scaffold splitting and feature generation) are carried out on the reactants only. If the atom-mapped reaction SMILES contain mapped hydrogens, enable explicit hydrogens via --explicit_h. Example of an atom-mapped reaction SMILES denoting the reaction of methanol to formaldehyde without hydrogens: [CH3:1][OH:2]>>[CH2:1]=[O:2] and with hydrogens: [C:1]([H:3])([H:4])([H:5])[O:2][H:6]>>[C:1]([H:3])([H:4])=[O:2].[H:5][H:6]. The reactions do not need to be balanced and can thus contain unmapped parts, for example leaving groups, if necessary.
An existing model, for example from training on a larger, lower quality dataset, can be used for parameter-initialization of a new model by providing a checkpoint of the existing model using either
--checkpoint_dir <dir>Directory where the model checkpoint(s) are saved (i.e.--save_dirduring training of the old model). This will walk the directory, and load all.ptfiles it finds.--checkpoint_path <path>Path to a model checkpoint file (.ptfile).
when training the new model. The model architecture of the new model should resemble the architecture of the old model - otherwise some or all parameters might not be loaded correctly. Please note that the old model is only used to initialize the parameters of the new model, but all parameters remain trainable (no frozen layers). Depending on the quality of the old model, the new model might only need a few epochs to train.
When training multitask models (models which predict more than one target simultaneously), sometimes not all target values are known for all molecules in the dataset. Chemprop automatically handles missing entries in the dataset by masking out the respective values in the loss function, so that partial data can be utilized, too. The loss function is rescaled according to all non-missing values, and missing values furthermore do not contribute to validation or test errors. Training on partial data is therefore possible and encouraged (versus taking out datapoints with missing target entries). No keyword is needed for this behavior, it is the default.
In contrast, when using sklearn_train.py (a utility script provided within Chemprop that trains standard models such as random forests on Morgan fingerprints via the python package scikit-learn), multi-task models cannot be trained on datasets with partially missing targets. However, one can instead train individual models for each task (via the argument --single_task), where missing values are automatically removed from the dataset. Thus, the training still makes use of all non-missing values, but by training individual models for each task, instead of one model with multiple output values. This restriction only applies to sklearn models (via :code:sklearn_train or :code:python sklearn_train.py), but NOT to default Chemprop models via chemprop_train or python train.py.
By default, the molecule objects created from each SMILES string are cached for all dataset sizes, and the graph objects created from each molecule object are cached for datasets up to 10000 molecules. If memory permits, you may use the keyword --cache_cutoff inf to set this cutoff from 10000 to infinity to always keep the generated graphs in cache (or to another integer value for custom behavior). This may speed up training (depending on the dataset size, molecule size, number of epochs and GPU support), since the graphs do not need to be recreated each epoch, but increases memory usage considerably. Below the cutoff, graphs are created sequentially in the first epoch. Above the cutoff, graphs are created in parallel (on --num_workers <int> workers) for each epoch. If training on a GPU, training without caching and creating graphs on the fly in parallel is often preferable. On CPU, training with caching if often preferable for medium-sized datasets and a very low number of CPUs. If a very large dataset causes memory issues, you might turn off caching even of the molecule objects via the commands --no_cache_mol to reduce memory usage further.
To load a trained model and make predictions, run predict.py and specify:
--test_path <path>Path to the data to predict on.- A checkpoint by using either:
--checkpoint_dir <dir>Directory where the model checkpoint(s) are saved (i.e.--save_dirduring training). This will walk the directory, load all.ptfiles it finds, and treat the models as an ensemble.--checkpoint_path <path>Path to a model checkpoint file (.ptfile).
--preds_pathPath where a CSV file containing the predictions will be saved.
For example:
chemprop_predict --test_path data/tox21.csv --checkpoint_dir tox21_checkpoints --preds_path tox21_preds.csv
or
chemprop_predict --test_path data/tox21.csv --checkpoint_path tox21_checkpoints/fold_0/model_0/model.pt --preds_path tox21_preds.csv
If installed from source, chemprop_predict can be replaced with python predict.py.
One method of obtaining the epistemic uncertainty of a prediction is to calculate the variance of an ensemble of models. To calculate these variances and write them as an additional column in the --preds_path file, use --ensemble_variance.
To load a trained model and encode the fingerprint latent representation of molecules, run fingerprint.py and specify:
--test_path <path>Path to the data to predict on.- A checkpoint by using either:
--checkpoint_dir <dir>Directory where the model checkpoint is saved (i.e.--save_dirduring training).--checkpoint_path <path>Path to a model checkpoint file (.ptfile).
--preds_pathPath where a CSV file containing the encoded fingerprint vectors will be saved.
SMILES from the provided file are encoded using the MPNN weights loaded from a trained checkpoint file. Fingerprint encoding uses the same set of arguments as making predictions. Unlike making predictions, fingerprint encoding only supports a single saved checkpoint file.
For example:
chemprop_fingerprint --test_path data/tox21.csv --checkpoint_dir tox21_checkpoints --preds_path tox21_fingerprint.csv
or
chemprop_fingerprint --test_path data/tox21.csv --checkpoint_path tox21_checkpoints/fold_0/model_0/model.pt --preds_path tox21_fingerprint.csv
If installed from source, chemprop_fingerprint can be replaced with python fingerprint.py.
It is often helpful to provide explanation of model prediction (i.e., this molecule is toxic because of this substructure). Given a trained model, you can interpret the model prediction using the following command:
chemprop_interpret --data_path data/tox21.csv --checkpoint_dir tox21_checkpoints/fold_0/ --property_id 1
If installed from source, chemprop_interpret can be replaced with python interpret.py.
The output will be like the following:
- The first column is a molecule and second column is its predicted property (in this case NR-AR toxicity).
- The third column is the smallest substructure that made this molecule classified as toxic (which we call rationale).
- The fourth column is the predicted toxicity of that substructure.
As shown in the first row, when a molecule is predicted to be non-toxic, we will not provide any rationale for its prediction.
| smiles | NR-AR | rationale | rationale_score |
|---|---|---|---|
| O=[N+]([O-])c1cc(C(F)(F)F)cc([N+](=O)[O-])c1Cl | 0.014 | ||
| CC1(C)O[C@@H]2C[C@H]3[C@@H]4C[C@H](F)C5=CC(=O)C=C[C@]5(C)[C@H]4[C@@H](O)C[C@]3(C)[C@]2(C(=O)CO)O1 | 0.896 | C[C@]12C=CC(=O)C=C1[CH2:1]C[CH2:1][CH2:1]2 | 0.769 |
| C[C@]12CC[C@H]3[C@@H](CC[C@@]45O[C@@H]4C(O)=C(C#N)C[C@]35C)[C@@H]1CC[C@@H]2O | 0.941 | C[C@]12C[CH:1]=[CH:1][C@H]3O[C@]31CC[C@@H]1[C@@H]2CC[C:1][CH2:1]1 | 0.808 |
| C[C@]12C[C@H](O)[C@H]3[C@@H](CCC4=CC(=O)CC[C@@]43C)[C@@H]1CC[C@]2(O)C(=O)COP(=O)([O-])[O-] | 0.957 | C1C[CH2:1][C:1][C@@H]2[C@@H]1[C@@H]1CC[C:1][C:1]1C[CH2:1]2 | 0.532 |
Chemprop's interpretation script explains model prediction one property at a time. --property_id 1 tells the script to provide explanation for the first property in the dataset (which is NR-AR). In a multi-task training setting, you will need to change --property_id to provide explanation for each property in the dataset.
For computational efficiency, we currently restricted the rationale to have maximum 20 atoms and minimum 8 atoms. You can adjust these constraints through --max_atoms and --min_atoms argument.
Please note that the interpreting framework is currently only available for models trained on properties of single molecules, that is, multi-molecule models generated via the --number_of_molecules command are not supported.
During training, TensorBoard logs are automatically saved to the same directory as the model checkpoints. To view TensorBoard logs, first install TensorFlow with pip install tensorflow. Then run tensorboard --logdir=<dir> where <dir> is the path to the checkpoint directory. Then navigate to http://localhost:6006.
We compared our model against MolNet by Wu et al. on all of the MolNet datasets for which we could reproduce their splits (all but Bace, Toxcast, and qm7). When there was only one fold provided (scaffold split for BBBP and HIV), we ran our model multiple times and reported average performance. In each case we optimize hyperparameters on separate folds, use rdkit_2d_normalized features when useful, and compare to the best-performing model in MolNet as reported by Wu et al. We did not ensemble our model in these results.
Results on regression datasets (lower is better)
| Dataset | Size | Metric | Ours | MolNet Best Model |
|---|---|---|---|---|
| QM8 | 21,786 | MAE | 0.011 ± 0.000 | 0.0143 ± 0.0011 |
| QM9 | 133,885 | MAE | 2.666 ± 0.006 | 2.4 ± 1.1 |
| ESOL | 1,128 | RMSE | 0.555 ± 0.047 | 0.58 ± 0.03 |
| FreeSolv | 642 | RMSE | 1.075 ± 0.054 | 1.15 ± 0.12 |
| Lipophilicity | 4,200 | RMSE | 0.555 ± 0.023 | 0.655 ± 0.036 |
| PDBbind (full) | 9,880 | RMSE | 1.391 ± 0.012 | 1.25 ± 0 |
| PDBbind (core) | 168 | RMSE | 2.173 ± 0.090 | 1.92 ± 0.07 |
| PDBbind (refined) | 3,040 | RMSE | 1.486 ± 0.026 | 1.38 ± 0 |
Results on classification datasets (higher is better)
| Dataset | Size | Metric | Ours | MolNet Best Model |
|---|---|---|---|---|
| PCBA | 437,928 | PRC-AUC | 0.335 ± 0.001 | 0.136 ± 0.004 |
| MUV | 93,087 | PRC-AUC | 0.041 ± 0.007 | 0.184 ± 0.02 |
| HIV | 41,127 | ROC-AUC | 0.776 ± 0.007 | 0.792 ± 0 |
| BBBP | 2,039 | ROC-AUC | 0.737 ± 0.001 | 0.729 ± 0 |
| Tox21 | 7,831 | ROC-AUC | 0.851 ± 0.002 | 0.829 ± 0.006 |
| SIDER | 1,427 | ROC-AUC | 0.676 ± 0.014 | 0.648 ± 0.009 |
| ClinTox | 1,478 | ROC-AUC | 0.864 ± 0.017 | 0.832 ± 0.037 |
Lastly, you can find the code to our original repo at https://github.com/wengong-jin/chemprop and for the Mayr et al. baseline at https://github.com/yangkevin2/lsc_experiments .