Question: benefit to using distance matrix?

[alexvpickering]

Hi I noticed that to_graph in dataset/get_qm8_data.py doesn't use the 7th channel (distance matrix). I presume this is related to this line in your paper:

Since some models cannot leverage feature on edges easily, we use the molecule graph itself as the only input information for all models so that it is a fair comparison.

I am wondering if you have tried including the 7th channel (or if it is even valid to do so)? So to_graph would return:

...
# return atom_feat, pair_feat[:, :, :6]
return atom_feat, pair_feat[:, :, :7]

If so, what would you suggest for generating distance matrices for other molecules (not from QM8 dataset)? Is AllChem.EmbedMolecule reasonable? Thank you for making your code available!

[lrjconan]

Hi,
Thanks for your interests in our work!

You are right that we do not use the edge feature, e.g., the distance matrix, due to the reason you quoted.

I have not tried adding the 7-th channel. For graph convolutional models, you could augment one more channel to the graph Laplacian as what I did in the code. To construct that channel, you can first use Gaussian kernel to construct the weighted adjacency matrix A[i,j] = exp(d[i,j] / sigma) where d is the distance. One thing to note that here I used the index based on 0. Therefore, pair_feat[:, :, 7] should be the distance matrix.

I am not familiar with this rdkit function AllChem.EmbedMolecule. I guess any sort of similarity measure between atoms should make sense here?

[alexvpickering]

Thank you for your response!

I'll let you know if I do try to adapt your algorithm to include the distance matrix. Cheers,