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DeepChem

Build Status Coverage Status

DeepChem aims to provide a high quality open-source toolchain that democratizes the use of deep-learning in drug discovery, materials science, and quantum chemistry.

Table of contents:

Requirements

Installation

Installation from source is the only currently supported format. deepchem currently supports both Python 2.7 and Python 3.5, but is not supported on any OS'es except 64 bit linux. Please make sure you follow the directions below precisely. While you may already have system versions of some of these packages, there is no guarantee that deepchem will work with alternate versions than those specified below.

Note that when using Ubuntu 16.04 server or similar environments, you may need to ensure libxrender is provided via e.g.:

sudo apt-get install -y libxrender-dev

Using a conda environment

You can install deepchem in a new conda environment using the conda commands in scripts/install_deepchem_conda.sh

git clone https://github.com/deepchem/deepchem.git      # Clone deepchem source code from GitHub
cd deepchem
bash scripts/install_deepchem_conda.sh deepchem
source activate deepchem
pip install tensorflow-gpu==1.0.1                       # If you want GPU support
python setup.py install                                 # Manual install
nosetests -v deepchem --nologcapture                    # Run tests

This creates a new conda environment deepchem and installs in it the dependencies that are needed. To access it, use the source activate deepchem command. Check this link for more information about the benefits and usage of conda environments. Warning: Segmentation faults can still happen via this installation procedure.

Easy Install via Conda

conda install -c omnia deepchem

Installing Dependencies Manually

  1. Download the 64-bit Python 2.7 or Python 3.5 versions of Anaconda for linux here. Follow the installation instructions

  2. rdkit

    conda install -c rdkit rdkit

  3. joblib

    conda install joblib

  4. six

    pip install six

  5. networkx

    conda install -c anaconda networkx=1.11

  6. mdtraj

    conda install -c omnia mdtraj

  7. pdbfixer

    conda install -c omnia pdbfixer=1.4

  8. tensorflow: Installing tensorflow on older versions of Linux (which have glibc < 2.17) can be very challenging. For these older Linux versions, contact your local sysadmin to work out a custom installation. If your version of Linux is recent, then the following command will work:

    pip install tensorflow-gpu

  9. deepchem: Clone the deepchem github repo:

    git clone https://github.com/deepchem/deepchem.git

    cd into the deepchem directory and execute

    python setup.py install

  10. To run test suite, install nosetests:

pip install nose

Make sure that the correct version of nosetests is active by running

which nosetests

You might need to uninstall a system install of nosetests if there is a conflict.

  1. If installation has been successful, all tests in test suite should pass: 
    nosetests -v deepchem --nologcapture
    Note that the full test-suite uses up a fair amount of memory. Try running tests for one submodule at a time if memory proves an issue.

Using a Docker Image

For major releases we will create docker environments with everything pre-installed. In order to get GPU support you will have to use the nvidia-docker plugin.

# This will the download the latest stable deepchem docker image into your images
docker pull deepchemio/deepchem

# This will create a container out of our latest image with GPU support
nvidia-docker run -i -t deepchemio/deepchem

# You are now in a docker container whose python has deepchem installed
# For example you can run our tox21 benchmark
cd deepchem/examples
python benchmark.py -d tox21

# Or you can start playing with it in the command line
pip install jupyter
ipython
import deepchem as dc

FAQ

  1. Question: I'm seeing some failures in my test suite having to do with MKL Intel MKL FATAL ERROR: Cannot load libmkl_avx.so or libmkl_def.so.

    Answer: This is a general issue with the newest version of scikit-learn enabling MKL by default. This doesn't play well with many linux systems. See BVLC/caffe#3884 for discussions. The following seems to fix the issue

    conda install nomkl numpy scipy scikit-learn numexpr
conda remove mkl mkl-service

Getting Started

The first step to getting started is looking at the examples in the examples/ directory. Try running some of these examples on your system and verify that the models train successfully. Afterwards, to apply deepchem to a new problem, try starting from one of the existing examples and modifying it step by step to work with your new use-case.

Input Formats

Accepted input formats for deepchem include csv, pkl.gz, and sdf files. For example, with a csv input, in order to build models, we expect the following columns to have entries for each row in the csv file.

  1. A column containing SMILES strings [1].
  2. A column containing an experimental measurement.
  3. (Optional) A column containing a unique compound identifier.

Here's an example of a potential input file.

Compound ID measured log solubility in mols per litre smiles
benzothiazole -1.5 c2ccc1scnc1c2

Here the "smiles" column contains the SMILES string, the "measured log solubility in mols per litre" contains the experimental measurement and "Compound ID" contains the unique compound identifier.

[2] Anderson, Eric, Gilman D. Veith, and David Weininger. "SMILES, a line notation and computerized interpreter for chemical structures." US Environmental Protection Agency, Environmental Research Laboratory, 1987.

Data Featurization

Most machine learning algorithms require that input data form vectors. However, input data for drug-discovery datasets routinely come in the format of lists of molecules and associated experimental readouts. To transform lists of molecules into vectors, we need to subclasses of DeepChem loader class dc.data.DataLoader such as dc.data.CSVLoader or dc.data.SDFLoader. Users can subclass dc.data.DataLoader to load arbitrary file formats. All loaders must be passed a dc.feat.Featurizer object. DeepChem provides a number of different subclasses of dc.feat.Featurizer for convenience.

Performances

  • Classification

Index splitting

Dataset Model Train score/ROC-AUC Valid score/ROC-AUC
clintox Logistic regression 0.967 0.676
Random forest 0.995 0.776
XGBoost 0.879 0.890
IRV 0.763 0.814
MT-NN classification 0.934 0.830
Robust MT-NN 0.949 0.827
Graph convolution 0.946 0.860
Weave 0.942 0.917
hiv Logistic regression 0.864 0.739
Random forest 0.999 0.720
XGBoost 0.917 0.745
IRV 0.841 0.724
NN classification 0.761 0.652
Robust NN 0.780 0.708
Graph convolution 0.876 0.779
Weave 0.907 0.753
muv Logistic regression 0.963 0.766
XGBoost 0.895 0.714
MT-NN classification 0.904 0.764
Robust MT-NN 0.934 0.781
Graph convolution 0.840 0.823
Weave 0.762 0.761
pcba Logistic regression 0.809 0.776
XGBoost 0.931 0.847
MT-NN classification 0.826 0.802
Robust MT-NN 0.809 0.783
Graph convolution 0.876 0.852
sider Logistic regression 0.933 0.620
Random forest 0.999 0.670
XGBoost 0.829 0.639
IRV 0.649 0.642
MT-NN classification 0.775 0.634
Robust MT-NN 0.803 0.632
Graph convolution 0.708 0.594
Weave 0.591 0.580
tox21 Logistic regression 0.903 0.705
Random forest 0.999 0.733
XGBoost 0.891 0.753
IRV 0.811 0.767
MT-NN classification 0.856 0.763
Robust MT-NN 0.857 0.767
Graph convolution 0.872 0.798
Weave 0.810 0.778
toxcast Logistic regression 0.721 0.575
XGBoost 0.738 0.621
MT-NN classification 0.830 0.678
Robust MT-NN 0.825 0.680
Graph convolution 0.821 0.720
Weave 0.766 0.715

Random splitting

Dataset Model Train score/ROC-AUC Valid score/ROC-AUC
bace_c Logistic regression 0.954 0.850
Random forest 0.999 0.939
IRV 0.876 0.871
NN classification 0.877 0.790
Robust NN 0.887 0.864
Graph convolution 0.906 0.861
Weave 0.807 0.780
bbbp Logistic regression 0.980 0.876
Random forest 0.999 0.918
IRV 0.904 0.917
NN classification 0.882 0.915
Robust NN 0.878 0.878
Graph convolution 0.962 0.897
Weave 0.929 0.934
clintox Logistic regression 0.972 0.725
Random forest 0.997 0.670
XGBoost 0.886 0.731
IRV 0.809 0.846
MT-NN classification 0.951 0.834
Robust MT-NN 0.959 0.830
Graph convolution 0.975 0.876
Weave 0.945 0.818
hiv Logistic regression 0.860 0.806
Random forest 0.999 0.850
XGBoost 0.933 0.841
IRV 0.839 0.809
NN classification 0.742 0.715
Robust NN 0.753 0.727
Graph convolution 0.847 0.803
Weave 0.902 0.825
muv Logistic regression 0.957 0.719
XGBoost 0.874 0.696
MT-NN classification 0.902 0.734
Robust MT-NN 0.933 0.732
Graph convolution 0.860 0.730
Weave 0.763 0.763
pcba Logistic regression 0.808 0.776
MT-NN classification 0.811 0.778
Robust MT-NN 0.811 0.771
Graph convolution 0.872 0.844
sider Logistic regression 0.929 0.656
Random forest 0.999 0.665
XGBoost 0.824 0.635
IRV 0.648 0.596
MT-NN classification 0.777 0.655
Robust MT-NN 0.804 0.630
Graph convolution 0.705 0.618
Weave 0.616 0.645
tox21 Logistic regression 0.902 0.715
Random forest 0.999 0.764
XGBoost 0.874 0.773
IRV 0.808 0.767
MT-NN classification 0.844 0.795
Robust MT-NN 0.855 0.773
Graph convolution 0.865 0.827
Weave 0.837 0.830
toxcast Logistic regression 0.725 0.586
XGBoost 0.738 0.633
MT-NN classification 0.836 0.684
Robust MT-NN 0.822 0.681
Graph convolution 0.820 0.717
Weave 0.757 0.729

Scaffold splitting

Dataset Model Train score/ROC-AUC Valid score/ROC-AUC
bace_c Logistic regression 0.957 0.729
Random forest 0.999 0.720
IRV 0.899 0.701
NN classification 0.897 0.743
Robust NN 0.910 0.747
Graph convolution 0.920 0.682
Weave 0.860 0.629
bbbp Logistic regression 0.980 0.959
Random forest 0.999 0.953
IRV 0.914 0.961
NN classification 0.899 0.961
Robust NN 0.908 0.956
Graph convolution 0.968 0.950
Weave 0.925 0.968
clintox Logistic regression 0.965 0.688
Random forest 0.993 0.735
XGBoost 0.873 0.850
IRV 0.793 0.718
MT-NN classification 0.937 0.828
Robust MT-NN 0.956 0.821
Graph convolution 0.965 0.900
Weave 0.950 0.947
hiv Logistic regression 0.858 0.798
Random forest 0.946 0.562
XGBoost 0.927 0.830
IRV 0.847 0.811
NN classification 0.775 0.765
Robust NN 0.785 0.748
Graph convolution 0.867 0.769
Weave 0.875 0.816
muv Logistic regression 0.947 0.767
XGBoost 0.875 0.705
MT-NN classification 0.899 0.762
Robust MT-NN 0.944 0.726
Graph convolution 0.872 0.795
Weave 0.780 0.773
pcba Logistic regression 0.810 0.742
MT-NN classification 0.814 0.760
Robust MT-NN 0.812 0.756
Graph convolution 0.874 0.817
sider Logistic regression 0.926 0.592
Random forest 0.999 0.619
XGBoost 0.796 0.560
IRV 0.639 0.599
MT-NN classification 0.776 0.557
Robust MT-NN 0.797 0.560
Graph convolution 0.722 0.583
Weave 0.600 0.529
tox21 Logistic regression 0.900 0.650
Random forest 0.999 0.629
XGBoost 0.881 0.703
IRV 0.823 0.708
MT-NN classification 0.863 0.703
Robust MT-NN 0.861 0.710
Graph convolution 0.885 0.732
Weave 0.866 0.773
toxcast Logistic regression 0.716 0.492
XGBoost 0.741 0.587
MT-NN classification 0.828 0.617
Robust MT-NN 0.830 0.614
Graph convolution 0.832 0.638
Weave 0.766 0.637
  • Regression
Dataset Model Splitting Train score/R2 Valid score/R2
bace_r Random forest Random 0.958 0.646
NN regression Random 0.898 0.680
Graphconv regression Random 0.760 0.676
Weave regression Random 0.523 0.577
Random forest Scaffold 0.956 0.201
NN regression Scaffold 0.897 0.208
Graphconv regression Scaffold 0.783 0.068
Weave regression Scaffold 0.602 0.018
chembl MT-NN regression Index 0.828 0.565
Graphconv regression Index 0.192 0.293
MT-NN regression Random 0.829 0.562
Graphconv regression Random 0.198 0.271
MT-NN regression Scaffold 0.843 0.430
Graphconv regression Scaffold 0.231 0.294
clearance Random forest Index 0.953 0.244
NN regression Index 0.884 0.211
Graphconv regression Index 0.696 0.230
Weave regression Index 0.261 0.107
Random forest Random 0.952 0.547
NN regression Random 0.880 0.273
Graphconv regression Random 0.685 0.302
Weave regression Random 0.229 0.129
Random forest Scaffold 0.952 0.266
NN regression Scaffold 0.871 0.154
Graphconv regression Scaffold 0.628 0.277
Weave regression Scaffold 0.228 0.226
delaney Random forest Index 0.953 0.626
XGBoost Index 0.898 0.664
NN regression Index 0.868 0.578
Graphconv regression Index 0.967 0.790
Weave regression Index 0.965 0.888
Random forest Random 0.951 0.684
XGBoost Random 0.927 0.727
NN regression Random 0.865 0.574
Graphconv regression Random 0.964 0.782
Weave regression Random 0.954 0.917
Random forest Scaffold 0.953 0.284
XGBoost Scaffold 0.890 0.316
NN regression Scaffold 0.866 0.342
Graphconv regression Scaffold 0.967 0.606
Weave regression Scaffold 0.976 0.797
hopv Random forest Index 0.943 0.338
MT-NN regression Index 0.725 0.293
Graphconv regression Index 0.307 0.284
Weave regression Index 0.046 0.026
Random forest Random 0.943 0.513
MT-NN regression Random 0.716 0.289
Graphconv regression Random 0.329 0.239
Weave regression Random 0.080 0.084
Random forest Scaffold 0.946 0.470
MT-NN regression Scaffold 0.719 0.429
Graphconv regression Scaffold 0.286 0.155
Weave regression Scaffold 0.097 0.082
kaggle MT-NN regression User-defined 0.748 0.452
lipo Random forest Index 0.960 0.483
NN regression Index 0.825 0.513
Graphconv regression Index 0.865 0.704
Weave regression Index 0.507 0.492
Random forest Random 0.958 0.518
NN regression Random 0.818 0.445
Graphconv regression Random 0.867 0.722
Weave regression Random 0.551 0.528
Random forest Scaffold 0.958 0.329
NN regression Scaffold 0.831 0.302
Graphconv regression Scaffold 0.882 0.593
Weave regression Scaffold 0.566 0.448
nci XGBoost Index 0.441 0.066
MT-NN regression Index 0.690 0.062
Graphconv regression Index 0.123 0.053
XGBoost Random 0.409 0.106
MT-NN regression Random 0.698 0.117
Graphconv regression Random 0.117 0.076
XGBoost Scaffold 0.445 0.046
MT-NN regression Scaffold 0.692 0.036
Graphconv regression Scaffold 0.131 0.036
pdbbind(core) Random forest Random 0.969 0.445
NN regression Random 0.973 0.494
pdbbind(refined) Random forest Random 0.963 0.511
NN regression Random 0.987 0.503
pdbbind(full) Random forest Random 0.965 0.493
NN regression Random 0.983 0.528
ppb Random forest Index 0.951 0.235
NN regression Index 0.902 0.333
Graphconv regression Index 0.673 0.442
Weave regression Index 0.418 0.301
Random forest Random 0.950 0.220
NN regression Random 0.903 0.244
Graphconv regression Random 0.646 0.429
Weave regression Random 0.408 0.284
Random forest Scaffold 0.943 0.176
NN regression Scaffold 0.902 0.144
Graphconv regression Scaffold 0.695 0.391
Weave regression Scaffold 0.401 0.373
qm7 NN regression Index 0.997 0.992
DTNN Index 0.997 0.995
NN regression Random 0.998 0.997
DTNN Random 0.999 0.998
NN regression Stratified 0.998 0.997
DTNN Stratified 0.998 0.998
qm7b MT-NN regression Index 0.903 0.789
DTNN Index 0.919 0.863
MT-NN regression Random 0.893 0.839
DTNN Random 0.924 0.898
MT-NN regression Stratified 0.891 0.859
DTNN Stratified 0.913 0.894
qm8 MT-NN regression Index 0.783 0.656
DTNN Index 0.857 0.691
MT-NN regression Random 0.747 0.660
DTNN Random 0.842 0.756
MT-NN regression Stratified 0.756 0.681
DTNN Stratified 0.844 0.758
qm9 MT-NN regression Index 0.733 0.766
DTNN Index 0.918 0.831
MT-NN regression Random 0.852 0.833
DTNN Random 0.942 0.948
MT-NN regression Stratified 0.764 0.792
DTNN Stratified 0.941 0.867
sampl Random forest Index 0.968 0.736
XGBoost Index 0.884 0.784
NN regression Index 0.917 0.764
Graphconv regression Index 0.982 0.903
Weave regression Index 0.993 0.948
Random forest Random 0.967 0.752
XGBoost Random 0.906 0.745
NN regression Random 0.908 0.711
Graphconv regression Random 0.987 0.868
Weave regression Random 0.992 0.888
Random forest Scaffold 0.966 0.477
XGBoost Scaffold 0.918 0.439
NN regression Scaffold 0.891 0.217
Graphconv regression Scaffold 0.985 0.666
Weave regression Scaffold 0.988 0.876
Dataset Model Splitting Train score/MAE(kcal/mol) Valid score/MAE(kcal/mol)
qm7 NN regression Index 11.0 12.0
NN regression Random 7.12 7.53
NN regression Stratified 6.61 7.34
  • General features

Number of tasks and examples in the datasets

Dataset N(tasks) N(samples)
bace_c 1 1522
bbbp 1 2053
clintox 2 1491
hiv 1 41913
muv 17 93127
pcba 128 439863
sider 27 1427
tox21 12 8014
toxcast 617 8615
bace_r 1 1522
chembl(5thresh) 691 23871
clearance 1 837
delaney 1 1128
hopv 8 350
kaggle 15 173065
lipo 1 4200
nci 60 19127
pdbbind(core) 1 195
pdbbind(refined) 1 3706
pdbbind(full) 1 11908
ppb 1 1614
qm7 1 7165
qm7b 14 7211
qm8 16 21786
qm9 15 133885
sampl 1 643

Time needed for benchmark test(~20h in total)

Dataset Model Time(loading)/s Time(running)/s
bace_c Logistic regression 10 10
NN classification 10 10
Robust NN 10 10
Random forest 10 80
IRV 10 10
Graph convolution 15 70
Weave 15 120
bbbp Logistic regression 20 10
NN classification 20 20
Robust NN 20 20
Random forest 20 120
IRV 20 10
Graph convolution 20 150
Weave 20 100
clintox Logistic regression 15 10
XGBoost 15 33
MT-NN classification 15 20
Robust MT-NN 15 30
Random forest 15 200
IRV 15 10
Graph convolution 20 130
Weave 20 90
hiv Logistic regression 180 40
XGBoost 180 1000
NN classification 180 350
Robust NN 180 450
Random forest 180 2800
IRV 180 200
Graph convolution 180 1300
Weave 180 2000
muv Logistic regression 600 450
XGBoost 600 3500
MT-NN classification 600 400
Robust MT-NN 600 550
Graph convolution 800 1800
Weave 800 4400
pcba Logistic regression 1800 10000
XGBoost 1800 470000
MT-NN classification 1800 9000
Robust MT-NN 1800 14000
Graph convolution 2200 14000
sider Logistic regression 15 80
XGBoost 15 660
MT-NN classification 15 75
Robust MT-NN 15 150
Random forest 15 2200
IRV 15 150
Graph convolution 20 50
Weave 20 200
tox21 Logistic regression 30 60
XGBoost 30 1500
MT-NN classification 30 60
Robust MT-NN 30 90
Random forest 30 6000
IRV 30 650
Graph convolution 30 160
Weave 30 300
toxcast Logistic regression 80 2600
XGBoost 80 30000
MT-NN classification 80 2300
Robust MT-NN 80 4000
Graph convolution 80 900
Weave 80 2000
bace_r NN regression 10 30
Random forest 10 50
Graphconv regression 10 110
Weave regression 10 150
chembl MT-NN regression 200 9000
Graphconv regression 250 1800
clearance NN regression 10 20
Random forest 10 10
Graphconv regression 10 60
Weave regression 10 70
delaney NN regression 10 40
XGBoost 10 50
Random forest 10 30
graphconv regression 10 40
Weave regression 10 40
hopv MT-NN regression 10 20
Random forest 10 50
Graphconv regression 10 50
Weave regression 10 60
kaggle MT-NN regression 2200 3200
lipo NN regression 30 60
Random forest 30 60
Graphconv regression 30 240
Weave regression 30 280
nci MT-NN regression 400 1200
XGBoost 400 28000
graphconv regression 400 2500
pdbbind(core) NN regression 0(featurized) 30
pdbbind(refined) NN regression 0(featurized) 40
pdbbind(full) NN regression 0(featurized) 60
ppb NN regression 20 30
Random forest 20 30
Graphconv regression 20 100
Weave regression 20 120
qm7 MT-NN regression 10 400
DTNN 10 600
qm7b MT-NN regression 10 600
DTNN 10 600
qm8 MT-NN regression 60 1000
DTNN 10 2000
qm9 MT-NN regression 220 10000
DTNN 10 14000
sampl NN regression 10 30
XGBoost 10 20
Random forest 10 20
graphconv regression 10 40
Weave regression 10 20

Gitter

Join us on gitter at https://gitter.im/deepchem/Lobby. Probably the easiest place to ask simple questions or float requests for new features.

DeepChem Publications

  1. Computational Modeling of β-secretase 1 (BACE-1) Inhibitors using Ligand Based Approaches
  2. Low Data Drug Discovery with One-Shot Learning
  3. MoleculeNet: A Benchmark for Molecular Machine Learning
  4. Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity

About Us

DeepChem is possible due to notable contributions from many people including Peter Eastman, Evan Feinberg, Joe Gomes, Karl Leswing, Vijay Pande, Aneesh Pappu, Bharath Ramsundar and Michael Wu (alphabetical ordering). DeepChem was originally created by Bharath Ramsundar with encouragement and guidance from Vijay Pande.

DeepChem started as a Pande group project at Stanford, and is now developed by many academic and industrial collaborators. DeepChem actively encourages new academic and industrial groups to contribute!

Corporate Supporters

DeepChem is supported by a number of corporate partners who use DeepChem to solve interesting problems.

Schrödinger

Schödinger

DeepChem has transformed how we think about building QSAR and QSPR models when very large data sets are available; and we are actively using DeepChem to investigate how to best combine the power of deep learning with next generation physics-based scoring methods.

DeepCrystal

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DeepCrystal was an early adopter of DeepChem, which we now rely on to abstract away some of the hardest pieces of deep learning in drug discovery. By open sourcing these efficient implementations of chemically / biologically aware deep-learning systems, DeepChem puts the latest research into the hands of the scientists that need it, materially pushing forward the field of in-silico drug discovery in the process.

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Deep-learning models for Drug Discovery and Quantum Chemistry

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