ATOM Modeling PipeLine (AMPL) for Drug Discovery

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.

Features

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

  • Data ingestion and curation

  • Featurization

  • Model training and tuning

  • Prediction generation

  • Visualization and analysis

  • Details of running specific features are within the parameter (options) documentation.

More detailed documentation is in the library documentation.

Built with

  • DeepChem: The basis for the graph convolution models

  • RDKit: Molecular informatics library

  • Mordred: Chemical descriptors

  • Other Python package dependencies

User guide

A step-by-step guide to getting started with MolVS.

API documentation