DeepChem
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. DeepChem is a package developed by the Pande group at Stanford and originally created by Bharath Ramsundar.
Table of contents:
- Requirements
- Installation
- FAQ
- Getting Started
- Contributing to DeepChem
- DeepChem Publications
- Corporate Supporters
- Examples
- About Us
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.
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.
Installing Dependencies Manually
-
Download the 64-bit Python 2.7 or Python 3.5 versions of Anaconda for linux here. Follow the installation instructions
-
rdkit
conda install -c rdkit rdkit
-
joblib
conda install joblib
-
six
pip install six
-
networkx
conda install -c anaconda networkx=1.11
-
mdtraj
conda install -c omnia mdtraj
-
pdbfixer
conda install -c omnia pdbfixer=1.4
-
tensorflow
: Installingtensorflow
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
-
deepchem
: Clone thedeepchem
github repo:git clone https://github.com/deepchem/deepchem.git
cd
into thedeepchem
directory and executepython setup.py install
-
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.
- If installation has been successful, all tests in test suite should pass:
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.
nosetests -v deepchem --nologcapture
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
-
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 issueconda 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.
- A column containing SMILES strings [1].
- A column containing an experimental measurement.
- (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
- Computational Modeling of β-secretase 1 (BACE-1) Inhibitors using Ligand Based Approaches
- Low Data Drug Discovery with One-Shot Learning
- MoleculeNet: A Benchmark for Molecular Machine Learning
- Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
About Us
DeepChem is a package by the Pande group at Stanford. DeepChem was originally created by Bharath Ramsundar, and has grown through the contributions of a number of undergraduate, graduate, and postdoctoral researchers working with the Pande lab.
Corporate Supporters
DeepChem is supported by a number of corporate partners who use DeepChem to solve interesting problems.
Schrödinger
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
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.
Version
1.1.0