We lifted 7 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ask-berkeley/coarsenconf | canonical | 0 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| check_distances | Not yet run | ask-berkeley/coarsenconf/utils/data_count.py code served (permissive licence) · get_code("d110c60c5ca0df4d") |
| get_layer_norm | Not yet run | ask-berkeley/coarsenconf/utils/model_utils.py code served (permissive licence) · get_code("68f46aa63c5fa264") |
| get_non_lin | Not yet run | ask-berkeley/coarsenconf/utils/model_utils.py code served (permissive licence) · get_code("ed468e0009c5b322") |
| get_norm | Not yet run | ask-berkeley/coarsenconf/utils/model_utils.py code served (permissive licence) · get_code("4f1f87e3765adc16") |
| glorot_init | Not yet run | ask-berkeley/coarsenconf/utils/equivariant_model_utils.py code served (permissive licence) · get_code("adfb39ff6429da01") |
| one_k_encoding | Not yet run | ask-berkeley/coarsenconf/utils/data_count.py code served (permissive licence) · get_code("6951294bc9abd0f3") |
| parallel_generate_smi | Not yet run | ask-berkeley/coarsenconf/generate.py code served (permissive licence) · get_code("fb06915e4ee93cb7") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Molecular conformer generation (MCG) is an important task in cheminformatics and drug discovery. The ability to efficiently generate low-energy 3D structures can avoid expensive quantum mechanical simulations, leading to accelerated virtual screenings and enhanced structural exploration. Several generative models have been developed for MCG, but many struggle to consistently produce high-quality conformers. To address these issues, we introduce CoarsenConf, which coarse-grains molecular graphs based on torsional angles and integrates them into an SE(3)-equivariant hierarchical variational autoencoder. Through equivariant coarse-graining, we aggregate the fine-grained atomic coordinates of subgraphs connected via rotatable bonds, creating a variable-length coarse-grained latent representation. Our model uses a novel aggregated attention mechanism to restore fine-grained coordinates from the coarse-grained latent representation, enabling efficient generation of accurate conformers. Furthermore, we evaluate the chemical and biochemical quality of our generated conformers on multiple downstream applications, including property prediction and oracle-based protein docking. Overall, CoarsenConf generates more accurate conformer ensembles compared to prior generative models.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2306.14852")
get_code_for_paper("2306.14852")
have("2306.14852")
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