AMPL-workshop-1

NCI Data Science Learning Exchange Hands-on Tutorial:

ATOM Modeling Pipeline (AMPL) for Drug Discovery

Presented by S. Ravichandran, Ph.D., BIDS, ATOM, Frederick National Laboratory for Cancer Research (FNLCR)

In this workshop, we will demonstrate how to use Atom Modeling PipeLine (AMPL; https://github.com/ATOMconsortium/AMPL), a new, 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 Machine-Learning (ML) steps with minimal intervention. In this workshop, we will cover the topics listed in the figure shown above. Future workshops will cover the next steps (modeling, inference) in drug discovery. The workshop on June 8 will include two parts, a short presentation followed by a hands-on tutorial.

Presentation Link: https://drive.google.com/file/d/1ScmouWOLOmzjw_NhNryyjHBqNXlVEw43/view?usp=sharing

To begin:

• Click, the following "Open in COLAB" badge,, to open the Google COLAB notebook

• If you have trouble with the previous step, click EDA_With_Harmonization_Using_AMPL.ipynb link shown above to open a browser tab and then click on the "Open in COLAB" batge to view the live notebook. To save the notebook in your Google Drive -make sure you are logged in with your Google account-, you need click on "Copy to Drive".

Supporting links

ATOM/AMPL software GitHub link:

• https://github.com/ATOMconsortium/AMPL

AMPL Software Publication:

• https://pubmed.ncbi.nlm.nih.gov/32243153/

AMPL Tutorials:

• https://github.com/ATOMconsortium/AMPL/tree/master/atomsci/ddm/examples/tutorials
• https://github.com/ravichas/AMPL-Tutorial

How to use AMPL:

• Download install on your local computers (conda-based Python software)
• Use Google COLAB; check out the following link on how to get-started, https://github.com/ATOMconsortium/AMPL/tree/master/atomsci/ddm/examples/tutorials
• Use on NIH Biowulf HPC systems, https://hpc.nih.gov/apps/ampl.html (NIH ID users only)

Similar chemoinformatics, drug-discovery software tools:

• DeepChem, https://deepchem.io/
• rdkit, https://www.rdkit.org/

Chemoinformatics databases:

• ChEMBL: https://www.ebi.ac.uk/chembl/
• PubChem: https://pubchem.ncbi.nlm.nih.gov/
• Drug Target Commons (DTC): https://drugtargetcommons.fimm.fi/
• ExCAPE-DB: https://solr.ideaconsult.net/search/excape/
• DrugBank: https://go.drugbank.com/

Small molecule drug-discovery related information:

• IC50, kd, ki: https://en.wikipedia.org/wiki/IC50
• SMILES: https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system
• Chemical Similarity: https://en.wikipedia.org/wiki/Chemical_similarity

Acknowledgements

• ATOM team