Comment by 392

I would reject the premise that the field of molecular/biological simulation is underexplored nor that existing approaches are "slower and harder to use than they need to be". This is a field that has been explored, in fact, by the most brilliant minds and the difficulty arises more in theoretical considerations (that is, devising algorithms to faithfully approximate the developed physics) rather than an obvious no-brainer application of AI.

The field of molecular and biological simulation is far more than simply "Newtonian mechanics". There is indeed a field called molecular dynamics (MD) that relies on "classical mechanics" yet it's defined usually in the Lagrangian formalism. Furthermore, there has been tons of work over the past few decades in developing more accurate numerical approximation algorithms. There is a ton of a theory in this field and if you're interested, the "MD Bible" is "Understanding Molecular Simulation" by Daan Frankel.

Now, MD is just the tip of the iceberg. Almost all chemistry simulations are built entirely from making subtle approximations to quantum mechanics and carefully building up frameworks. For example, Hartree-Fock theory (HF), Density Functional Theory (DFT), Couple Cluster theory (CCSD(T)), etc. Then there is a field known colloquially as semi-empirical methods which are a sort of combination of the above two methods. And that's just on the side of chemical simulations (i.e. I'm excluding physics-specific simulations etc).

And now, more recently there has been effort in building machine-learned interatomic potentials, machine-learned density functionals, equivariant graph neural networks, etc etc.

If you're still interested in these class of problems, consider trying to build a good model for OMol25: https://arxiv.org/abs/2505.08762