[aheifets]

As you might expect, there are trade-offs, and it's a question of picking the right tool for the job.

My understanding of D.E. Shaw's approach is that they're doing molecular dynamics, i.e. simulation. You get to watch the motion of every atom in the system. That allows for a close investigation of a given protein's movements, which is great especially if you're trying to learn about its biology. Unfortunately, it is rather computationally expensive; while I don't know DESRES's latest stats, I've seen reports on large parallel MD simulations completing about once per day.

In contrast, we've posed the question of binding as a machine learning problem. Neural networks are computationally expensive to train, but make predictions quickly. Our system can assess millions of protein-drug pairs per day, since we're not simulating the motion of every atom. You don't get to watch what each atom is doing, but you get insight into the behavior of lots of potential medicines.