SMILES-X

Autonomous molecular compounds characterization for small datasets without descriptors

Guillaume Lambard (1), Ekaterina Gracheva (2,3)
<sub>1. Research and Services Division of Materials Data and Integrated System, Energy Materials Design Group, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan.
2. International Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki, 305-0044 Japan.
3. University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577 Japan.</sub>

On arXiv: arXiv:1906.09938 [physics.comp-ph]

What is it?

The SMILES-X is an autonomous pipeline that finds best neural architectures to predict a physicochemical property from molecular SMILES only (see OpenSMILES). No human-engineered descriptors are needed. The SMILES-X has been especially designed for small datasets (<< 1000 samples).

Who can use the SMILES-X?

Researchers/engineers/students in the fields of materials science, physicochemistry, drug discovery and related fields

Which kind of data can be used?

The SMILES-X is dedicated to small datasets (<< 1000 samples) of (molecular SMILES, experimental/simulated property)

What can I do with it?

With the SMILES-X, you can:

• Design specific neural architectures fitted to your small dataset via Bayesian optimization.
• Predict molecular properties of a list of SMILES based on designed models ensembling without human-engineered descriptors.
• Interpret a prediction by visualizing the salient elements and/or substructures most related to a property

What is the efficiency of the SMILES-X on benchmark datasets?

• ESOL: logarithmic aqueous solubility (mols/L) for 1128 organic small molecules.
• FreeSolv: calculated and experimental hydration free energies (kcal/mol) for 642 small neutral molecules in water.
• Lipophilicity: experimental data on octanol/water distribution coefficient (logD at pH 7.4) for 4200 compounds.

All these datasets are available in the validation_data/ directory above.

Dependencies

You must have an access to at least 1 NVIDIA GPU with:

• CUDA >= 9.0
• cuDNN >= 7.3.0

For now, the SMILES-X has been successfully runned on Titan(Xp, V, V100, P100), GTX 1660 and RTX 2070/80 NVIDIA GPUs.

For a good start, follow the RDKit installation guide for installing the RDKit via conda.
Then, install the following dependencies in your RDKit conda environment (e.g. my-rdkit-env):

• python >= 3.6
• pandas >= 0.24.2
• numpy >= 1.16.4
• matplotlib >= 3.1.0
• GPy >= 1.9.8
• GPyOpt >= 1.2.5
• scikit-learn >= 0.21.2
• adjustText >= 0.7.3
• scipy >= 1.3.0
• Keras >= 2.2.4
• tensorflow-gpu >= 1.12.0

Usage

The following instruction is a summary from the python notebooks SMILESX_Prediction_github.ipynb and SMILESX_Visualization_github.ipynb available above. Please feel free to download, use and modify them.

• Copy and paste the directory called SMILESX into your working directory
• Use the following basic import to your jupyter notebook

import pandas as pd
from SMILESX import main, interpret, inference
%matplotlib inline

How to find the best architectures for my dataset?

• After basic libraries import, unfold your dataset

validation_data_dir = "./validation_data/"
extension = '.csv'
data_name = 'FreeSolv' # FreeSolv, ESOL, Lipophilicity or your own dataset
prop_tag = 'expt' # which column corresponds to the property to infer in the *.csv file

sol_data = pd.read_csv(validation_data_dir+data_name+extension)
sol_data = sol_data[['smiles',prop_tag]] # reduce the data to (SMILES, property) sets

If the column containing the SMILES has a different name, feel free to change it accordingly

• Define architectural hyper-parameters bounds to be used for the neural architecture search

dhyp_range = [int(2**itn) for itn in range(3,11)] # 
dalpha_range = [float(ialpha/10.) for ialpha in range(20,40,1)] # Adam's learning rate = 10^(-dalpha_range)

bounds = [
    {'name': 'lstmunits', 'type': 'discrete', 'domain': dhyp_range},  # number of LSTM units
    {'name': 'denseunits', 'type': 'discrete', 'domain': dhyp_range}, # number of Dense units
    {'name': 'embedding', 'type': 'discrete', 'domain': dhyp_range},  # number of Embedding dimensions
    {'name': 'batchsize', 'type': 'discrete', 'domain': dhyp_range},  # batch size per epoch during training
    {'name': 'lrate', 'type': 'discrete', 'domain': dalpha_range}     # Adam's learning rate 10^(-dalpharange) 
]

These bounds are used in the paper, but they can be tuned according to your dataset

• Let the SMILES-X find the best architectures for the most accurate property predictions

main.Main(data=sol_data,        # provided data (SMILES, property)
          data_name=data_name,  # dataset's name
          data_units='',        # property's SI units
          bayopt_bounds=bounds, # bounds contraining the Bayesian search of neural architectures
          k_fold_number = 10,   # number of k-folds used for cross-validation
          augmentation = True,  # SMILES augmentation
          outdir = "./data/",  # directory for outputs (plots + .txt files)
          bayopt_n_epochs = 10, # number of epochs for training during Bayesian search
          bayopt_n_rounds = 25, # number of architectures to sample during Bayesian search 
          bayopt_on = True,     # use Bayesian search
          n_gpus = 1,           # number of GPUs to be used
          patience = 25,        # number of epochs with no improvement after which training will be stopped
          n_epochs = 100)       # maximum of epochs for training

Please refer to the SMILESX/main.py for a detailed review of the options

How to infer a property on new data (SMILES)?

• Just use

pred_from_ens = inference.Inference(data_name=data_name, 
                                    smiles_list = ['CC','CCC','C=O'], # new list of SMILES to characterize
                                    data_units = '',
                                    k_fold_number = 3,                # number of k-folds used for inference
                                    augmentation = True,              # with SMILES augmentation
                                    outdir = "./data/")

It returns a table of SMILES with their inferred property (mean, standard deviation) determined by models ensembling, e.g.

How to interpret a prediction?

• Just use

interpret.Interpretation(data=sol_data, 
                         data_name=data_name, 
                         data_units='', 
                         k_fold_number = 3,
                         k_fold_index = 0,               # model id to use for interpretation
                         augmentation = True, 
                         outdir = "./data/", 
                         smiles_toviz = 'Cc1ccc(O)cc1C', # SMILES to interpret
                         font_size = 15,                 # plots font parameter
                         font_rotation = 'vertical')     # plots font parameter

Returns:

• 1D attention map on individual tokens

• 2D attention map on individual vertices and edges

• Temporal relative distance to the final prediction, or evolution of the inference with sequential addition of tokens along a SMILES

Please refer to the article for further details

How to cite the SMILES-X?

@misc{lambard2019smilesx,
    title={SMILES-X: autonomous molecular compounds characterization for small datasets without descriptors},
    author={Guillaume Lambard and Ekaterina Gracheva},
    year={2019},
    eprint={1906.09938},
    archivePrefix={arXiv},
    primaryClass={physics.comp-ph}
}