ATOM Modeling PipeLine (AMPL) for Drug Discovery


Created by the Accelerating Therapeutics for Opportunites in Medicine (ATOM) Consortium

AMPL is an open-source, modular, extensible software pipeline for building and sharing models to advance in silico drug discovery.

The ATOM Modeling PipeLine (AMPL) extends the functionality of DeepChem and supports an array of machine learning and molecular featurization tools. AMPL is an end-to-end data-driven modeling pipeline to generate machine learning models that can predict key safety and pharmacokinetic-relevant parameters. AMPL has been benchmarked on a large collection of pharmaceutical datasets covering a wide range of parameters.

A pre-print of a manuscript describing this project is available through ArXiv. readthedocs are available as well here.  

Public release

This release marks the first public availability of the ATOM Modeling PipeLine (AMPL). Installation instructions for setting up and running AMPL are described below. Basic examples of model fitting and prediction are also included. AMPL has been deployed to and tested in multiple computing environments by ATOM Consortium members. Detailed documentation for the majority of the available features is included, but the documentation does not cover all developed features. This is a living software project with active development. Check back for continued updates. Feedback is welcomed and appreciated, and the project is open to contributions!

Table of contents

Useful links

Getting started

Welcome to the ATOM Modeling PipeLine (AMPL) for Drug Discovery! These instructions will explain how to install this pipeline for model fitting and prediction.


AMPL is a Python 3 package that has been developed and run in a specific conda environment. The following prerequisites are necessary to install AMPL:


Clone the git repository

git clone

Create conda environment

cd conda

conda create -y -n atomsci --file conda_package_list.txt

conda activate atomsci

pip install -r pip_requirements.txt

Install AMPL

Go to the AMPL root directory and install the AMPL package:

conda activate atomsci

cd ..

./ && ./

More installation information

Example AMPL usage

An example Jupyter notebook is available to get you started: atomsci/ddm/Delaney_Example.ipynb  


AMPL includes a suite of software tests. This section explains how to run a very simple test that is fast to run. The Python test fits a random forest model using Mordred descriptors on a set of compounds from Delaney, et al with solubility data. A molecular scaffold-based split is used to create the training and test sets. In addition, an external holdout set is used to demonstrate how to make predictions on new compounds.

To run the Delaney Python script that curates a dataset, fits a model, and makes predictions, run the following commands:

conda activate atomsci

cd atomsci/ddm/test/integrative/delaney_RF


The important files for this test are listed below:

More example and test information

AMPL Features

AMPL enables tasks for modeling and prediction from data ingestion to data analysis and can be broken down into the following stages:

  1. Data ingestion and curation
  2. Featurization
  3. Model training and tuning
  4. Prediction generation
  5. Visualization and analysis

1. Data curation

2. Featurization

3. Model training and tuning

4. Supported models

5. Visualization and analysis

Details of running specific features are within the parameter (options) documentation. More detailed documentation is in the library documentation.

Running AMPL

AMPL can be run from the command line or by importing into Python scripts and Jupyter notebooks.

Python scripts and Jupyter notebooks

AMPL can be used to fit and predict molecular activities and properties by importing the appropriate modules. See the examples for more descriptions on how to fit and make predictions using AMPL.

Pipeline parameters (options)

AMPL includes many parameters to run various model fitting and prediction tasks.

Library documentation

AMPL includes detailed docstrings and comments to explain the modules. Full HTML documentation of the Python library is available with the package at atomsci/ddm/docs/build/html/index.html.

More information on AMPL usage

Advanced AMPL usage

Command line

AMPL can fit models from the command line with:

python --config_file test.json


Hyperparameter optimization

Hyperparameter optimization for AMPL model fitting is available to run on SLURM clusters. Examples of running hyperparameter optimization will be added.

Advanced installation


AMPL has been developed and tested on the following Linux systems:


To remove AMPL from a conda environment use:

conda activate atomsci
pip uninstall atomsci-ampl


To remove the atomsci conda environment entirely from a system use:

conda deactivate
conda remove --name atomsci --all


Advanced testing

Running all tests

To run the full set of tests, use Pytest from the test directory:

conda activate atomsci

cd atomsci/ddm/test



Running SLURM tests

Several of the tests take some time to fit. These tests can be submitted to a SLURM cluster as a batch job. Example general SLURM submit scripts are included as

conda activate atomsci

cd atomsci/ddm/test/integrative/delaney_NN


cd ../../../..

cd atomsci/ddm/test/integrative/wenzel_NN



Running tests without internet access

AMPL works without internet access. Curation, fitting, and prediction do not require internet access.

However, the public datasets used in tests and examples are not included in the repo due to licensing concerns. These are automatically downloaded when the tests are run.

If a system does not have internet access, the datasets will need to be downloaded before running the tests and examples. From a system with internet access, run the following shell script to download the public datasets. Then, copy the AMPL directory to the offline system.

cd atomsci/ddm/test


cd ../../..

# Copy AMPL directory to offline system



Installing the AMPL for development

To install the AMPL for development, use the following commands instead:

conda activate atomsci
./ && ./


This will create a namespace package in your conda directory that points back to your git working directory, so every time you reimport a module you'll be in sync with your working code. Since site-packages is already in your sys.path, you won't have to fuss with PYTHONPATH or setting sys.path in your notebooks.


Versions are managed through GitHub tags on this repository.

Built with

Project information


The Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium

  1. Lawrence Livermore National Laboratory
  2. GlaxoSmithKline Inc.
  3. Frederick National Laboratory for Cancer Research
  4. Computable


Please contact the AMPL repository owners for bug reports, questions, and comments.


Thank you for contributing to AMPL!


AMPL is distributed under the terms of the MIT license. All new contributions must be made under this license.

See MIT license and NOTICE for more details.