Speeding up drug discovery with diffusion generative models

With the release of platforms like DALL-E 2 and Midjourney, diffusion generative models have achieved mainstream popularity, owing to their ability to generate a series of absurd, breathtaking, and often meme-worthy images from text prompts like "teddy bears working on new AI research on the moon in the 1980s." But a team of researchers at MIT's Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic) thinks there could be more to diffusion generative models than just creating surreal images -- they could accelerate the development of new drugs and reduce the likelihood of adverse side effects. A paper introducing this new molecular docking model, called DiffDock, will be presented at the 11th International Conference on Learning Representations. The model's unique approach to computational drug design is a paradigm shift from current state-of-the-art tools that most pharmaceutical companies use, presenting a major opportunity for an overhaul of the traditional drug development pipeline. Drugs typically function by interacting with the proteins that make up our bodies, or proteins of bacteria and viruses.