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.

Table of contents:

• Requirements
• Installation
  • Conda Environment
  • Direct from Source
  • Docker
• FAQ
• Getting Started
  • Input Formats
  • Data Featurization
  • Performances
• Contributing to DeepChem
  • Code Style Guidelines
  • Documentation Style Guidelines
  • Gitter
• DeepChem Publications
• Corporate Supporters
  • Schrödinger
  • DeepCrystal
• Examples
• About Us

Requirements

• pandas
• rdkit
• boost
• joblib
• sklearn
• numpy
• six
• mdtraj
• tensorflow

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.

11. 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

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