Open puririshi98 opened 1 week ago
Would like to contribute to this paper. Listed what to do, need some discussion for the details^^.
GINConv
directlytorch_geometric.nn.attention.qformer
torch_geometric.nn.nlp.llm
torch_geometric.nn.attention.qformer
torch_geometric.nn.nlp.llm
Is there any existing dataset? Or we need to extract and clean from PubChem from scratch.
Hey @xnuohz sorry for the delay! Just had a quick look at the paper, and it looks like they haven't published the code and dataset that they curated for the paper, but as a general goal, we should aim for reproducing the result from the paper by re-implementing the dataset, preprocessing, and model with an example script.
We can also discuss this in PyG Slack :)
(cc'ing @puririshi98 for when he's back)
@xnuohz I think they seem to follow the this data preprocessing step https://github.com/chao1224/MoleculeSTM/tree/main/data as described in section 3.2
Also the 1D Graph Branch
should be 1D SMILES Branch
which uses the encoder designed to encode SMILES string https://github.com/seyonechithrananda/bert-loves-chemistry
🚀 The feature, motivation and pitch
Paper: https://ai4d3.github.io/papers/34.pdf Part of the community sprint https://github.com/pyg-team/pytorch_geometric/issues/9694 The goal of this project is to reproduce the work done in MoleculeGPT while tying it as closely to the existing GNN+LLM frameworks in PyG. We recommend using as many existing features as possible from PyG. Additional features which you feel will be reusable for other workflows should be added to PyG. One-off functions that are specific to this workflow can be left inside the example. Most of the effort will likely go into building a PyG dataset that matches the one described in the paper. At a high level the dataset is a composition of Q+A pairs for molecular field, with matching molecules as context. These Q+A pairs focus on molecular property prediction.
Alternatives
No response
Additional context
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