Month: July 2021

Enlarge (credit: Aurich Lawson | Getty Images)

We may have bitten off more than we could chew, folks.

An Amazon engineer told me that when he heard what I was trying to do with Ars headlines, the first thing he thought was that we had chosen a deceptively hard problem. He warned that I needed to be careful about properly setting my expectations. If this was a real business problem... well, the best thing he could do was suggest reframing the problem from "good or bad headline" to something less concrete.

That statement was the most family-friendly and concise way of framing the outcome of my four-week, part-time crash course in machine learning. As of this moment, my PyTorch kernels aren't so much torches as they are dumpster fires. The accuracy has improved slightly, thanks to professional intervention, but I am nowhere near deploying a working solution. Today, as I am allegedly on vacation visiting my parents for the first time in over a year, I sat on a couch in their living room working on this project and accidentally launched a model training job locally on the Dell laptop I brought—with a 2.4 GHz Intel Core i3 7100U CPU—instead of in the SageMaker copy of the same Jupyter notebook. The Dell locked up so hard I had to pull the battery out to reboot it.

Enlarge (credit: Sloan-Kettering)

Just one week after Google's DeepMind AI group finally described its biology efforts in detail, the company is releasing a paper that explains how it analyzed nearly every protein encoded in the human genome and predicted its likely three-dimensional structure—a structure that can be critical for understanding disease and designing treatments. In the very near future, all of these structures will be released under a Creative Commons license via the European Bioinformatics Institute, which already hosts a major database of protein structures.

In a press conference associated with the paper's release, DeepMind's Demis Hassabis made clear that the company isn't stopping there. In addition to the work described in the paper, the company will release structural predictions for the genomes of 20 major research organisms, from yeast to fruit flies to mice. In total, the database launch will include roughly 350,000 protein structures.

What’s in a structure?

We just described DeepMind's software last week, so we won't go into much detail here. The effort is an AI-based system trained on the structure of existing proteins that had been determined (often laboriously) through laboratory experiments. The system uses that training, plus information it obtains from families of proteins related by evolution, to predict how a protein's chain of amino acids folds up in three-dimensional space.