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.
```deepchem``` currently requires Python 2.7, and is not supported on any platforms except 64bit 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. This is especially true for the `keras` library, where `deepchem` is known not to work with the latest default version.
1. Download the **64-bit** Python 2.7 or Python 3.5 versions of Anaconda for linux [here](https://www.continuum.io/downloads#_unix).
1. Anaconda 2.7
Download the **64-bit Python 2.7** version of Anaconda for linux [here](https://www.continuum.io/downloads#_unix).
Follow the [installation instructions](http://docs.continuum.io/anaconda/install#linux-install)
2.`openbabel`
@@ -54,35 +68,41 @@ Linux (64-bit) Installation
conda install joblib
```
7. `theano`
7.`keras`
```bash
conda install -c omnia theano
pipinstall keras --user
```
8. `keras`
`deepchem` only supports the `tensorflow` backend for keras. To set the backend to `tensorflow`,
add the following line to your `~/.bashrc`
```bash
conda install -c omnia keras
export KERAS_BACKEND=tensorflow
```
See [keras docs](https://keras.io/backend/) for more details and alternate methods of setting backend.
9. `six`
8.`six`
```bash
conda install six
```
10. `dill`
9.`dill`
```bash
conda install dill
```
11. `ipyparallel`
10.`ipyparallel`
```bash
conda install ipyparallel
```
12. `mdtraj`
11.`mdtraj`
```bash
conda install-c omnia mdtraj
```
12.`scikit-learn`
```bash
conda install scikit-learn
```
13.`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
15. If installation has been successful, all tests in test suite should pass:
16. To run test suite, install `nosetests`:
```bash
pip install nose --user
```
Make sure that the correct version of `nosetests` is active by running
```bash
which nosetests
```
You might need to uninstall a system install of `nosetests` if
there is a conflict.
17. If installation has been successful, all tests in test suite should pass:
```bash
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.
## 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
2. Question: The test suite is core-dumping for me. What's up?
```
[rbharath]$ nosetests -v deepchem --nologcapture
Illegal instruction (core dumped)
```
Answer: This is often due to `openbabel` issues on older linux systems. Open `ipython` and run the following
```
In [1]: import openbabel as ob
```
If you see a core-dump, then it's a sign there's an issue with your `openbabel` install. Try reinstalling `openbabel` from source for your machine.
## 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 |
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 use the ``deechem``
featurization class ``DataFeaturizer``. Instances of this class must be
passed a ``Featurizer`` object. ``deepchem`` provides a number of
different subclasses of ``Featurizer`` for convenience:
## Contributing to DeepChem
We actively encourage community contributions to DeepChem. The first place to start getting involved is by running our examples locally. Afterwards, we encourage contributors to give a shot to improving our documentation. While we take effort to provide good docs, there's plenty of room for improvement. All docs are hosted on Github, either in this `README.md` file, or in the `docs/` directory.
Once you've got a sense of how the package works, we encourage the use of Github issues to discuss more complex changes, raise requests for new features or propose changes to the global architecture of DeepChem. Once consensus is reached on the issue, please submit a PR with proposed modifications. All contributed code to DeepChem will be reviewed by a member of the DeepChem team, so please make sure your code style and documentation style match our guidelines!
### Code Style Guidelines
DeepChem broadly follows the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html). In terms of practical changes, the biggest effect is that all code uses 2-space indents instead of 4-space indents. We encourage new contributors to make use of [pylint](https://www.pylint.org/). Aim for a score of at least 8/10 on contributed files.
### Documentation Style Guidelines
DeepChem uses [NumPy style documentation](https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt). Please follow these conventions when documenting code, since we use [Sphinx+Napoleon](http://www.sphinx-doc.org/en/stable/ext/napoleon.html) to automatically generate docs on [deepchem.io](deepchem.io).
## DeepChem Publications
1. [Computational Modeling of β-secretase 1 (BACE-1) Inhibitors using
DeepChem is a package by the [Pande group](https://pande.stanford.edu/) at Stanford. DeepChem was originally created by [Bharath Ramsundar](http://rbharath.github.io/), and has grown through the contributions of a number of undergraduate, graduate, and postdoctoral researchers working with the Pande lab.
