Pre-computed files need to be regenerated for each set of parameters

[shervinea]

Context. Real-time PDB parsing with the BioPython package, e.g. typically:

enzynet/enzynet/pdb.py

Line 53 in 31d30e0

self.structure = PDBParser().get_structure(pdb_id.upper(), fullfilename)

is expensive and bottlenecks the training process if done on the fly.

For this reason, we put in place a "precomputation stage"

enzynet/enzynet/volume.py

Line 123 in 31d30e0

def check_precomputed(self) -> None:

that takes all enzymes beforehand and stores target volumes in a dedicated folder.

Current limitation. This process is repeated for each set of parameters {weights considered, interpolation level between atoms p, volume size}. This is ineffective from the perspectives of:

• total computations performed: PDB parsing is the same for all these configurations and needs to be identically repeated for each of them. The only remaining operations are relatively cheap: e.g. 2D -> 3D mapping, points interpolation. With a proper implementation, these last steps can easily be done on the fly without becoming a bottleneck.
• space: the number/size of produced files increases with the same pace as the number of configurations that the user tries out (!).

Desired behavior. Coordinates + weights precomputation from PDB files is done only once and produces a parsed version of the data that is:

1. Light enough so that it can be transformed to target volumes on the fly
2. Complete enough so that all configurations' data can be derived from them.