Computer Vision News Computer Vision News 10 of each position and modifying existing proteins. “Basically, a lot of generative models are very bad at doing that,” Nathan points out. “Coming up with a good, robust, sample-efficient method for generating these discrete sequences was the problem we were interested in.” The paper represents these sequences as discrete characters that look like language but are onehot encoded to make an image representation, where the pixels tell you what character is in each position. The team then applies a noising and denoising process. This approach is already used to create new molecules, aid in sequence diversification, and hit expansion in the real world, which are critical phases in drug development. “What we’re trying to do is take some starting protein sequences and use the generative model to explore around them to find other real proteins that are reasonable and interesting to look at,” Nathan tells us. “That’s part one of a much bigger process called lab-in-theloop for protein design.” Still coming down from his Outstanding Paper Award win, Nathan says he is excited about the growing acceptance of machine learning in biology. “As far as I can tell, this is the first bio ML paper that’s won an outstanding paper award at ICLR – maybe at any major machine learning conference,” he reflects. It is a fantastic achievement with only five Outstanding Paper winners out of 7,262 submitted and 2,260 accepted research papers. What does he think convinced the judges that it deserved such an accolade? “I would guess that, if anything, it’s the real-world impact! What we did is not in the mainstream of generative modeling right now. There’s an aspect of going against the grain, developing something new, and actually showing that we needed to develop something new for real-world impact.” This work has been a collaboration between Nathan and his co-authors, Dan Berenberg and Saeed Saremi, as well as many other scientists and engineers across Prescient and Genentech. “One of many things ICLR Outstanding Paper
RkJQdWJsaXNoZXIy NTc3NzU=