5 ms·

The things that Schrodinger tried are still valid physics, just for different systems. He didn't know that, but now we do, this has little bearing on modern phy

by mhh__ 5y ago

The things that Schrodinger tried are still valid physics, just for different systems. He didn't know that, but now we do, this has little bearing on modern physics. Also chemists have the luxury of agreeing because the fundamentals of their work (not the day to day act of doing chemistry) gets to assume the presence of physics i.e. it's stampkeeping (depending on who you ask).

jleyank 5y ago
Not quite stamp collecting, although botany or anatomy head in that direction. Call chemistry “just applied physics” as engineering is “just applied science”.
- mhh__ 5y ago
  I prefer to call engineering "physics ruined by philistines" (sometimes at least, engineers seem to have a unique ability to take beautiful physics and notate it in the most cumbersome "safe" way possible)
- dekhn 5y ago
  It's a quote ascribed to Rutherford ("all science is physics or just stamp collecting"). I also heard it when studying protein structures in the 90s (the idea being, if you're just a crystallographer cranking out more structures, you're doing stamp collecting; the real reason to determine structures is to elucidate the general principles of protein folding and function). Note that protein structure prediction finally fell to machine learning and it was "mostly stamp collecting" (IE, the accurate predictions come from subtle analysis of rich protein sequence alignments, not from understanding the fundamental principles of protein folding).
  - alar44 5y ago
    We absolutely understand protein folding. The fact there isn't a closed form simple solution to tell us the shape of a protein shouldn't be surprising at all. It's just hard to calculate. It's like the 3 body problem, but there are 90. I don't know why you think there would be some simple solution to that that doesn't involve simulation and heavy computation. You'd need 8 dimensional 100x100 matrices or something to describe something like a protein in pure math, which is way more like programming than arithmetic anyway.
    - dekhn 5y ago
      We do not understand protein folding. I'm an expert in this field with publications and what you just said makes no sense at all. The simplified protein folding problem states that proteins fold to their energy minimum, and a succesful heuristic can find the "correct" fold by finding the structure argmin(energy). Although this is a real simplification that doesn't represent actual proteins, even that is not something you'd solve with matrices; combinatorial math. More importantly, "protein folding" is the biophysical process that proteins experience; what DeepMind did was solve "structure prediction", which is another simpler problem, and they didn't do it by energy minimization, they did it by exploiting sequence similarity to provide structural constraints that massively reduced the search space.
      - alar44 5y ago
        I used matrix math as a hand wavey example of how one might purely numerically describe the angles in a structure. What don't we understand? We know the forces of all the atoms that act on each other, what is the mystery at this point other than calculating it is hard?
        dekhn 5y ago
        "We know the forces of all the atoms that act on each other, what is the mystery at this point other than calculating it is hard?" might be true, but it's computationally intractable and even if you did find a way around it you'd just learn that your force field was inaccurate, or that proteins don't actually fold to their energy minimum. Not just hard, but NP-hard. In fact, the "protein folding problem is NP hard" is misleading; in fact, the "static protein structure problem" is NP hard, and protein folding is "harder" than that (it's a superproblem of static structure prediction). The way to describe angles for a protein is this: you use degrees or radians to describe the torsion angles of the backbone (2 torsions per amino acid, 360 possible positions per torsion, times the number of backbone atoms in the protein). Treat side chains with rotamers, that's already a solved problem. However, you can't compute self-collisions in angle space you need to embed the protein structure in cartesian coordinates, which is how they are normally represented (as a graph representing the bond topology, and an N*3 array of positions).

by mhh__ 5y ago

jleyank 5y ago

Not quite stamp collecting, although botany or anatomy head in that direction. Call chemistry “just applied physics” as engineering is “just applied science”.

mhh__ 5y ago
I prefer to call engineering "physics ruined by philistines" (sometimes at least, engineers seem to have a unique ability to take beautiful physics and notate it in the most cumbersome "safe" way possible)
dekhn 5y ago
It's a quote ascribed to Rutherford ("all science is physics or just stamp collecting"). I also heard it when studying protein structures in the 90s (the idea being, if you're just a crystallographer cranking out more structures, you're doing stamp collecting; the real reason to determine structures is to elucidate the general principles of protein folding and function). Note that protein structure prediction finally fell to machine learning and it was "mostly stamp collecting" (IE, the accurate predictions come from subtle analysis of rich protein sequence alignments, not from understanding the fundamental principles of protein folding).
- alar44 5y ago
  We absolutely understand protein folding. The fact there isn't a closed form simple solution to tell us the shape of a protein shouldn't be surprising at all. It's just hard to calculate. It's like the 3 body problem, but there are 90. I don't know why you think there would be some simple solution to that that doesn't involve simulation and heavy computation. You'd need 8 dimensional 100x100 matrices or something to describe something like a protein in pure math, which is way more like programming than arithmetic anyway.
  - dekhn 5y ago
    We do not understand protein folding. I'm an expert in this field with publications and what you just said makes no sense at all. The simplified protein folding problem states that proteins fold to their energy minimum, and a succesful heuristic can find the "correct" fold by finding the structure argmin(energy). Although this is a real simplification that doesn't represent actual proteins, even that is not something you'd solve with matrices; combinatorial math. More importantly, "protein folding" is the biophysical process that proteins experience; what DeepMind did was solve "structure prediction", which is another simpler problem, and they didn't do it by energy minimization, they did it by exploiting sequence similarity to provide structural constraints that massively reduced the search space.
    - alar44 5y ago
      I used matrix math as a hand wavey example of how one might purely numerically describe the angles in a structure. What don't we understand? We know the forces of all the atoms that act on each other, what is the mystery at this point other than calculating it is hard?
      - dekhn 5y ago
        "We know the forces of all the atoms that act on each other, what is the mystery at this point other than calculating it is hard?" might be true, but it's computationally intractable and even if you did find a way around it you'd just learn that your force field was inaccurate, or that proteins don't actually fold to their energy minimum. Not just hard, but NP-hard. In fact, the "protein folding problem is NP hard" is misleading; in fact, the "static protein structure problem" is NP hard, and protein folding is "harder" than that (it's a superproblem of static structure prediction). The way to describe angles for a protein is this: you use degrees or radians to describe the torsion angles of the backbone (2 torsions per amino acid, 360 possible positions per torsion, times the number of backbone atoms in the protein). Treat side chains with rotamers, that's already a solved problem. However, you can't compute self-collisions in angle space you need to embed the protein structure in cartesian coordinates, which is how they are normally represented (as a graph representing the bond topology, and an N*3 array of positions).