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  • Created 08 Sep 2021
ATOM Modeling Pipeline (AMPL) for Drug Discovery - A hands-on tutorial – 
Tuesday, September 14, 2021 @ 02:00 pm EDT — Tuesday, September 14, 2021 @ 03:30 pm EDT
Do you want to know how to use Machine Learning (ML) for accelerating drug discovery? Join us on September 14, 2:00 pm – 3:30 pm ET for the second workshop on using Machine Learning (ML) to accelerate drug discovery! The workshop focuses on using the Atom Modeling PipeLine (AMPL), an open-source conda-based software that automates key drug discovery steps. AMPL is designed to take molecular binding data (ex., IC50, ki, etc.) and carry out the ML steps with minimal user intervention (see the figure shown above). The first workshop held in June highlighted AMPL’s capabilities for creating ML-ready datasets.

Date: Tuesday, Sep 14, 2021

Time: 2:00 p.m – 3:30 p.m. ET

Location: Webex

Registration: Not required

Presenter: Sarangan Ravichandran, PhD, PMP Senior Data Scientist,
ATOM Consortium/Frederick National Laboratory for Cancer Research (FNLCR) and Adjunct Professor in Bioinformatics, Hood College

Supporting materials: Tutorial and AMPL: A Data-Driven Modeling Pipeline for Drug Discovery

The second workshop on September 14 will demonstrate three preliminary in-silico drug discovery topics:

  • data ingestion

  • cleaning/tidying

  • curation on AMPL

  • Note: This session will be 90 minutes and will use Google COLAB notebooks (compatible to Jupyter notebooks) for demonstration. Please see the outline below:

    Notebook-1: Ingestion, Cleaning and Exploratory Data Analysis(EDA) of Binding Assay Data (30 minutes)

    Issues associated with data ingestion and curation (data sources: Drug Data Commons; ChEMBL and ExCAPE-DB)
    Exploratory data analysis of the ingested datasets
    Standardization of outcome units such as IC50 (etc. um to nM)
    Data visualization and comparison
    Notebook-2: Standardization of SMILES, Featurization and Compound Overlap/Diversity using a Python Jupyter Notebook (30 minutes)

    Compound overlap
    SMILES standardization
    Explore compound diversity using featurization and Tanimoto distance
    Create plots/heatmaps for analysis
    Notebook-3: Curate, Merge Datasets to Create the Final ML-ready Dataset (30 minutes)

    Removal of duplicates
    Filter extreme data
    Merge the DTC, ChEMBL, and ExCAPE-DB datasets to create a curated dataset
    Data curation on the merged data
    Creation of ML-ready dataset
    To learn more about the software, visit the AMPL GitHub repository at this link

    Questions? Contact the "> NCI Data Science Learning Exchange
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