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What?
Instead of En-transformer refiner, IPA (https://github.com/lucidrains/invariant-point-attention) refiner has been integrated.
Why?
To see the effectiveness of different experiments. IPA has lied in the core of AF2 structure module. It is worthy to give a try.
How?
Simply follow existing RGN2_transformer implementation. The difference is output of coordinates instead of angles. However, the compatibility of angles (used by losses) has been done via discrete Frenet-Serret equations.
Testing?
dRMSD can reach around 5. Experimental hyper-para's include "structure_module_depth" and other IPA parameters. Running script has been included.
Screenshots
At the moment the xtension branch of mp_nerf repo has been integrated via source code. Some functions (e.g. mp_nerf.ml_utils.backbone_forcefield()) has not been implemented. Not a big deal. I leave it for further improvement.
Anything Else?
Integrate LSTM output of 4 angles has been discussed but not implemented. LSTM can give nice prediction of secondary structures. Worthy to give a trial.