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Comment by MAXPOOL | Hacker News Reader

Advances in computational chemistry and computational materials science. Specially using ML to speed up computation. Computational chemistry is already patchwork of different heuristic approaches. Graph neural networks and even some language models seem very promising addition.

If we could simulate and observe what happens with complex chemistry accurately, it would completely change the biology, medicine and materials science.

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> If we could simulate and observe what happens with complex chemistry accurately, it would completely change the biology, medicine and materials science.

That's probably far less true than you imagine. See Derek Lowe's take on it: https://blogs.sciencemag.org/pipeline/archives/2021/03/19/ai...

The rate-limiting steps in drug discovery is in figuring out a) what you need to muck up to improve health or b) how to muck it up without mucking up other things badly enough to kill you. Computational chemistry has generally focused more on solving problems c) how to muck up this target more effectively or d) how to make the mucker-upper in the first place, which, while not useless, is not going to be a revolutionary change by any stretch of the imagination.

>If we could simulate and observe what happens with complex chemistry accurately

There is the rub. Biological simulations have been written for 40 years now. It's an extremely difficult problem considering how many latent variables are at play, and people have been working on it for a very long time now.

I suspect that the recent breakthrough in protein structure prediction is just a start.

ML techniques will be used to cut that latent variable space in both quantum chemistry and molecular mechanics based methods.

A lot of my work revolves around human physiology. I think a lot of that will be improved with very accurate, high resolution, and continuous sensors. When that happens (with BioMEMS of course for most cases), AI will be able to fill in a gap, in a lot of various applications.

In the early 2010s, I was an undergraduate electrical engineering student with type 1 diabetes playing around with such models by reprogramming stuff that had been presented in peer-reviewed journal articles. I eventually programmed a closed loop control system (also known as "Artificial Pancreas System") as a spring break project to inform my insulin dosages. Mostly, it was a soul-searching project as engineering school was physically enduring for me, as I have serious health problems. I had found a paper about a sliding mode control with respect to type 1 diabetes that looked solvable to me, but I did not know if it was actually solvable. I decided to see what I could do with it, and I was successful, in 2011 and barely old enough to legally drink!

Anyways, I can assure you that while research on control systems is drying up, including for physiological systems, that the excitement is just about to begin for what you mention, starting in about 5 years.

Yes! This would be fantastic, I'd love to see this happen.

If anyone is working on this, and is looking to hire a computational organic chemist-turned-ai engineer, let me know!

https://www.schrodinger.com/careers

There are many others.

Wonderful, thanks!

Contact info?

Thanks, I realized I never filled in my profile. Mail can go to anything at rombouts dot email.

I have a relative who is a PhD chemist who I talked to about some of this stuff. He is generally skeptical of it because "the interesting chemicals are the ones that have extreme properties and predicting extreme properties is hard". It's a lot easier to model normal behavior than unusual behavior. He wasn't totally against the idea, he just thought it would be much harder than many people assume.

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