Drug Discovery ML Pipelines Explained (explained with Coronavirus example)
Common Pitfalls of ML people going into Bio (and how to avoid them)
UPDATE : At the request of a client, the Github repo detailing the full backend for a drug discovery pipeline has been set to private, with access being granted to only specific individuals. The outline will still be available publically.
The Environment
Bio-specific libraries
In the bioinformatics/cheminformatics space, there are plenty of libraries and tools out there that might be new to you.
Albumentations - If you’re doing anything with microscopy or radiography, you should definitely make sure to include the albumentations library in your setup. This was a library built to contain many of the common image augmentations used by Kaggle-competition-winning teams.
DeepChem -
Running your First Experiments
Forking and Contributing
Cited as:
@article{mcateer2020ddmlcovid,
title = "Drug Discovery ML Pipelines Explained (explained with Coronavirus example)",
author = "McAteer, Matthew",
journal = "matthewmcateer.me",
year = "2020",
url = "https://matthewmcateer.me/blog/drug-discovery-ml-covid/"
}If you notice mistakes and errors in this post, don’t hesitate to contact me at [contact at matthewmcateer dot me] and I will be very happy to correct them right away! Alternatily, you can follow me on Twitter and reach out to me there.
See you in the next post 😄