About this episode
Our Slack community wanted to hear about AI-driven drug discovery, and we listened. Abraham Heifets from Atomwise joins us for a fascinating deep dive into the intersection of deep learning models and molecule binding. He describes how these methods work and how they are beginning to help create drugs for “undruggable” diseases! Join the discussion Changelog++ members save 4 minutes on this episode because they made the ads disappear. Join today! Sponsors: O'Reilly Media – Learn by doing — Python, data, AI, machine learning, Kubernetes, Docker, and more. Just open your browser and dive in. Learn more and keep your teams’ skills sharp at oreilly.com/changelog Code-ish by Heroku – A podcast from the team at Heroku, exploring code, technology, tools, tips, and the life of the developer. Check out episode 98 and episode 99 for insights on the ethical and technical sides of deep fakes. Subscribe on Apple Podcasts and Spotify . The Brave Browser – Browse the web up to 8x faster than Chrome and Safari, block ads and trackers by default, and reward your favorite creators with the built-in Basic Attention Token . Download Brave for free and give tipping a try right here on changelog.com . Featuring: Abe Heifets – X Chris Benson – Website , GitHub , LinkedIn , X Daniel Whitenack – Website , GitHub , X Show Notes: Atomwise Atomwise Receives a $2.3M Grant to Develop New Therapies for Drug Resistant Malaria and Tuberculosis Atomwise Partners with Global Research Teams to Pursue Broad-Spectrum Treatments Against COVID-19 and Future Coronavirus Outbreaks World robotic soccer Philadelphia chromosome Alphafold Canavan disease example: Paper: “Discovery of Novel Inhibitors of a Critical Brain Enzyme Using a Homology Model and a Deep Convolutional Neural Network” AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery [“Memorizing yesterday’s stock price” example](Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization) Something missing or broken? PRs welcome!