We lifted 8 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| deeplearningps/ecconf | canonical | 5 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| Atoms_to_Idx | Ran | deeplearningps/ecconf/EcConf/graphs/mol.py code served (permissive licence) · get_code("8d4c20476da4cb96") |
| pad_dims_like | Ran | deeplearningps/ecconf/EcConf/model/consistency/consistency_models.py code served (permissive licence) · get_code("97baf73b6f52c910") |
| parallel_cpkt_to_single | Ran | deeplearningps/ecconf/EcConf/model/ecmodel.py code served (permissive licence) · get_code("1e7788c8c8cd4027") |
| timesteps_schedule | Ran | deeplearningps/ecconf/EcConf/model/consistency/consistency_models.py code served (permissive licence) · get_code("39e629bdee9e75df") |
| update_ema_model | Ran | deeplearningps/ecconf/EcConf/model/consistency/consistency_models.py code served (permissive licence) · get_code("ece13d42e1169655") |
| Adjs_to_Onek | Not yet run | deeplearningps/ecconf/EcConf/graphs/mol.py code served (permissive licence) · get_code("90939d7c019d88a4") |
| Atoms_to_Onek | Not yet run | deeplearningps/ecconf/EcConf/graphs/mol.py code served (permissive licence) · get_code("fa9fcee6c72baf13") |
| Statistic_GPARAMS | Not yet run | deeplearningps/ecconf/EcConf/graphs/datasets.py code served (permissive licence) · get_code("60954ca4b6de1b21") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Despite recent advancement in 3D molecule conformation generation driven by diffusion models, its high computational cost in iterative diffusion/denoising process limits its application. In this paper, an equivariant consistency model (EC-Conf) was proposed as a fast diffusion method for low-energy conformation generation. In EC-Conf, a modified SE (3)-equivariant transformer model was directly used to encode the Cartesian molecular conformations and a highly efficient consistency diffusion process was carried out to generate molecular conformations. It was demonstrated that, with only one sampling step, it can already achieve comparable quality to other diffusion-based models running with thousands denoising steps. Its performance can be further improved with a few more sampling iterations. The performance of EC-Conf is evaluated on both GEOM-QM9 and GEOM-Drugs sets. Our results demonstrate that the efficiency of EC-Conf for learning the distribution of low energy molecular conformation is at least two magnitudes higher than current SOTA diffusion models and could potentially become a useful tool for conformation generation and sampling. We release our code at https://github.com/zhi520/EcConf.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2308.00237")
get_code_for_paper("2308.00237")
have("2308.00237")
Connect an agent — have() is free.