Adding table of contents.

DeepChem

=============

# DeepChem

DeepChem aims to provide a high quality open-source toolchain for

democratizing the use of deep-learning in drug discovery and quantum

chemistry. DeepChem

is a package developed by the [Pande group](https://pande.stanford.edu/) at

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](https://pande.stanford.edu/) at

Stanford and originally created by [Bharath Ramsundar](http://rbharath.github.io/). 

Requirements

------------

### Table of contents:

* [Requirements](#requirements)

* [Installation from Source](#installation)

* [FAQ](#faq)

* [Getting Started](#getting-started)

    * [Input Formats](#input-formats)

    * [Data Featurization](#data-featurization)

* [DeepChem Publications](#deepchem-publications)

* [Examples](/examples)

* [About Us](#about-us)

## Requirements

* [openbabel](http://openbabel.org/wiki/Main_Page)

* [pandas](http://pandas.pydata.org/)

* [rdkit](http://www.rdkit.org/docs/Install.html)

* [mdtraj](http://mdtraj.org/)

* [tensorflow](https://www.tensorflow.org/)

Linux (64-bit) Installation from Source

---------------------------------------

## Installation

```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.

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.

1. Download the **64-bit** Python 2.7 or Python 3.5 versions of Anaconda for linux [here](https://www.continuum.io/downloads#_unix). 

    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.

Frequently Asked Questions

--------------------------

## 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.```

   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

---------------

## 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

-------------

### 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.

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

notation and computerized interpreter for chemical structures." US

Environmental Protection Agency, Environmental Research Laboratory, 1987.

Data Featurization

------------------

### Data Featurization

Most machine learning algorithms require that input data form vectors.

However, input data for drug-discovery datasets routinely come in the

passed a ``Featurizer`` object. ``deepchem`` provides a number of

different subclasses of ``Featurizer`` for convenience:

DeepChem Publications

---------------------

## DeepChem Publications

1. [Computational Modeling of β-secretase 1 (BACE-1) Inhibitors using

Ligand Based

Approaches](http://pubs.acs.org/doi/abs/10.1021/acs.jcim.6b00290)

About Us

--------

## About Us

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.