Cover art for podcast The Machine Learning Podcast.__init__

The Machine Learning Podcast.__init__

100 EpisodesProduced by Tobias MaceyWebsite

The podcast the constantly changing world of machine learning and data science

44:30

Accelerating Drug Discovery Using Machine Learning With TorchDrug

Summary

Finding new and effective treatments for disease is a complex and time consuming endeavor, requiring a high degree of domain knowledge and specialized equipment. Combining his expertise in machine learning and graph algorithms with is interest in drug discovery Jian Tang created the TorchDrug project to help reduce the amount of time needed to find new candidate molecules for testing. In this episode he explains how the project is being used by machine learning researchers and biochemists to collaborate on finding effective treatments for real-world diseases.

Announcements
  • Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
  • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
  • Your host as usual is Tobias Macey and today I’m interviewing Jian Tang about TorchDrug
Interview
  • Introductions
  • How did you get introduced to Python?
  • Can you describe what TorchDrug is and the story behind it?
  • What are the goals of the TorchDrug project?
    • Who are the target users of the project?
    • What are the main ways that it is being used?
  • What are the challenges faced by biologists and chemists working on development and discovery of pharmaceuticals?
    • What are some of the other tools/techniques that they would use (in isolation or combination with TorchDrug)?
  • Can you describe how TorchDrug is implemented?
    • How have you approached the design of the project and its APIs to make it accessible to engineers that don’t possess domain expertise in drug discovery research?
  • How do graph structures help when modeling and experimenting with chemical structures for drug discovery?
  • What are the formats and sources of data that you are working with?
    • What are some of the complexities/challenges that you have had to deal with to integrate with up or downstream systems to fit into the overall research process?
  • Can you talk through the workflow of using TorchDrug to build and validate a model?
    • What is involved in determining and codifying a goal state for the model to optimize for?
  • What are the biggest open questions in the area of drug discovery and research?
    • How is TorchDrug being used to assist in the exploration of those problems?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on TorchDrug?
  • When is TorchDrug the wrong choice?
  • What do you have planned for the future of TorchDrug?
Keep In Touch Picks
  • Tobias
    • Rope refactoring library
  • Jian
    • Attending conferences once the pandemic is over
Closing Announcements
  • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
  • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
  • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Links

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Educational emoji reaction

Educational

Interesting emoji reaction

Interesting

Funny emoji reaction

Funny

Agree emoji reaction

Agree

Love emoji reaction

Love

Wow emoji reaction

Wow

Listen to The Machine Learning Podcast.__init__

RadioPublic

A free podcast app for iPhone and Android

  • User-created playlists and collections
  • Download episodes while on WiFi to listen without using mobile data
  • Stream podcast episodes without waiting for a download
  • Queue episodes to create a personal continuous playlist
RadioPublic on iOS and Android
Or by RSS
RSS feed
https://www.pythonpodcast.com/feed/mp3/

Connect with listeners

Podcasters use the RadioPublic listener relationship platform to build lasting connections with fans

Yes, let's begin connecting
Browser window

Find new listeners

  • A dedicated website for your podcast
  • Web embed players designed to convert visitors to listeners in the RadioPublic apps for iPhone and Android
Clicking mouse cursor

Understand your audience

  • Capture listener activity with affinity scores
  • Measure your promotional campaigns and integrate with Google and Facebook analytics
Graph of increasing value

Engage your fanbase

  • Deliver timely Calls To Action, including email acquistion for your mailing list
  • Share exactly the right moment in an episode via text, email, and social media
Icon of cellphone with money

Make money

  • Tip and transfer funds directly to podcastsers
  • Earn money for qualified plays in the RadioPublic apps with Paid Listens