Liwaiwai Liwaiwai



Liwaiwai Liwaiwai
  • /
  • Artificial Intelligence
  • Machine Learning
  • Robotics
  • Engineering
    • Architecture
    • Design
    • Software
    • Hybrid Cloud
    • Data
  • About
  • Artificial Intelligence
  • Data

How To Use AI To Discover New Drugs And Materials With Limited Data

  • April 15, 2022
  • relay

IBM Research is working on new ways to generate material designs with AI with dozens of examples for the training model, instead of the tens of thousands often required.

Over the last few years, we’ve seen that advances in deep-learning Learn about how using generative models to come up with new ideas, we can dramatically accelerate the pace at which we can discover new molecules, materials, drugs, and more.generative AI models can lead to amazing new ways to discover new molecules for drug and materials discovery. These models, when trained effectively, can provide the spark of inspiration for uncovering molecular combinations that scientists may never have considered trying, or ideas that would’ve taken years to figure out. What in the past may have taken decades to discover can, in some cases, now be achieved in a matter of months, such as our work accelerating the pace at which we can discover new antimicrobial peptides.

But generative models like these generally require large amounts of training data to learn. This requires time and a lot of energy, and in some cases, there just isn’t enough data available to adequately train the models. By treating molecules as graphs, however, and learning the grammar of the graph, we developed a method that can require tens to hundreds of training examples, rather than the deep learning models that can require up to nearly 100,000 examples. This lets us generate candidates for testing faster and more flexibly, shortening the pipeline for creating new materials, such as pharmaceuticals.

The work, to be presented at the 2022 International Conference on Learning Representations1 (ICLR), dives into this method of molecular generation. Jie Chen, a researcher at the MIT-IBM Watson AI Lab, recently sat down with Thomas Asche, R&D Digitalization lead at Evonik Industries, to discuss with IBM Research’s Shaheen Parks how their work can be used to discover new polymers.

Watch: Using AI to discover new drugs and materials with limited data

If you’d like to read Jie Chen’s presentation he discussed in the video, click here.

relay

Related Topics
  • IBM
  • IBM Watson
  • MIT
  • MIT-IBM Watson AI Lab
You May Also Like
View Post
  • Artificial Intelligence

Microsoft‘s Big AI Ambitions Go Beyond Just OpenAI And ChatGPT

  • February 3, 2023
View Post
  • Artificial Intelligence
  • Technology

Deepfakes: Faces Created By AI Now Look More Real Than Genuine photos

  • February 3, 2023
View Post
  • Artificial Intelligence

GPT-3 In Your Pocket? Why Not!

  • February 3, 2023
View Post
  • Artificial Intelligence
  • Design
  • Engineering

Can AI Replace Cloud Architects?

  • February 2, 2023
View Post
  • Artificial Intelligence

Meet Aiko And Aiden: The World’s First AI Interns

  • February 2, 2023
View Post
  • Artificial Intelligence
  • Technology

Google Scrambles To Catch Up In The Wake Of OpenAI’s ChatGPT

  • January 31, 2023
View Post
  • Artificial Intelligence
  • Technology

9 Ways We Use AI In Our Products

  • January 31, 2023
View Post
  • Artificial Intelligence
  • Technology

7 Ways Google Is Using AI To Help Solve Society’s Challenges

  • January 30, 2023
Stay Connected!
LATEST
  • 1
    Microsoft‘s Big AI Ambitions Go Beyond Just OpenAI And ChatGPT
    • February 3, 2023
  • 2
    Deepfakes: Faces Created By AI Now Look More Real Than Genuine photos
    • February 3, 2023
  • 3
    GPT-3 In Your Pocket? Why Not!
    • February 3, 2023
  • 4
    Can AI Replace Cloud Architects?
    • February 2, 2023
  • 5
    Meet Aiko And Aiden: The World’s First AI Interns
    • February 2, 2023
  • 6
    Google Scrambles To Catch Up In The Wake Of OpenAI’s ChatGPT
    • January 31, 2023
  • 7
    9 Ways We Use AI In Our Products
    • January 31, 2023
  • 8
    Google Cloud Unveils New AI Tools for Retailers
    • January 31, 2023
  • 9
    7 Ways Google Is Using AI To Help Solve Society’s Challenges
    • January 30, 2023
  • 10
    The Ethics Of Machine Learning: Understanding The Role Of Developers And Designers
    • January 30, 2023

about
About
Hello World!

We are liwaiwai.com. Created by programmers for programmers.

Our site aims to provide materials, guides, programming how-tos, and resources relating to artificial intelligence, machine learning and the likes.

We would like to hear from you.

If you have any questions, enquiries or would like to sponsor content, kindly reach out to us at:

[email protected]

Live long & prosper!
Most Popular
  • 1
    8 Best Human Behaviour Datasets For Machine Learning
    • January 30, 2023
  • 2
    Built With BigQuery: How To Accelerate Data-Centric AI Development With Google Cloud And Snorkel AI
    • January 29, 2023
  • 3
    What Kind Of Future Will AI Bring Enterprise IT?
    • January 29, 2023
  • 4
    Prompt Engineering For ChatGPT And Generative AI
    • January 29, 2023
  • 5
    AI Might Be Seemingly Everywhere, But There Are Still Plenty Of Things It Can’t Do—for now
    • January 27, 2023
  • /
  • Artificial Intelligence
  • Machine Learning
  • Robotics
  • Engineering
  • About

Input your search keywords and press Enter.