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Mismatch in Arguments for eps_net and Issues with Test Results Reproduction #4

@BL-Lac149597870

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@BL-Lac149597870

Hello,

I encountered an issue with the function eps_net during the inference process here L274-L277. It appears that the number of arguments expected by the forward() method of eps_net does not match the number passed in the code.

Upon inspecting the code, I found that the argument R_t_global was not being passed. I tried to fix this by adding the following line, mimicking the training part of the code:

_, R_t_global = global_frame(X_t, mask_gen_pos)

and then passed the variable R_t_global to the eps_net as:

vp_t, vr_t, vd_t, vc_t = self.eps_net(
                    d_t, s_t, X_t, R_t, R_t_global, res_feat, pair_feat, t_tensor, 
                    mask_gen_d, mask_gen_aa, mask_gen_pos, mask_res
                )   # (N, L, 3), (N, L, 3, 3), (N, L, 3)

As a result, the sample function looks like this:

X_t = manifold_to_euclid(r_t, p_t, d_t, X_ctx, mask_gen_pos)

# WARN: fix missed R_t_global
_, R_t_global = global_frame(X_t, mask_gen_pos)
# ===========================

X_t, R_t = X_t[:, :, BBHeavyAtom.CA], construct_3d_basis(X_t[:, :, BBHeavyAtom.CA],
                                                         X_t[:, :, BBHeavyAtom.C],
                                                         X_t[:, :, BBHeavyAtom.N],)

vp_t, vr_t, vd_t, vc_t = self.eps_net(
        d_t, s_t, X_t, R_t, R_t_global, res_feat, pair_feat, t_tensor, 
        mask_gen_d, mask_gen_aa, mask_gen_pos, mask_res
    )   # (N, L, 3), (N, L, 3, 3), (N, L, 3)         

1、Could you please confirm if the changes I made to the code are correct?


I then used this fix to generate peptides with the test dataset using pretrained weights from the ppflow_pretrained.pt ckpt and evaluated the generated peptides(bb4.pdb format) with evaluation/eval_struct_seq.py. However, the results I obtained did not correspond with the results presented in your paper.

Model ΔG(↓) IMP%-B(↑) IMP%-S(↑) Validity(↑) Novelty(↑) Diversity
PPFLOW-BB -349.59 36.02% 10.34% 1.00 0.84 0.76
reproduction - - 0.04% 1.00 0.97 0.64

If it helps, corresponding raw files and evaluation meta files can be downloaded here.

2、Could there be any additional minor errors in the code that are preventing the results from being accurately reproduced?

I really appreciate your help in resolving this issue. Thank you for your continued support and dedication to improving this project!

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