Yoshua Bengio, Jian Peng, Minkai Xu, Jian Tang, Shitong Luo
We lifted 4 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| deepgraphlearning/cgcf-confgen | reimplementation | 2 of 2 |
| luost26/CGCF-ConfGen | — | 0 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| reduce_tensor | Ran | deepgraphlearning/cgcf-confgen/models/cnf_edge/cnf.py pointer only (licence: NONE) · get_code("11965d5ebfe77481") |
| stable_var | Ran | deepgraphlearning/cgcf-confgen/models/cnf_edge/cnf.py pointer only (licence: NONE) · get_code("27ca363b776a43f8") |
| ODEfunc | Not yet run | luost26/CGCF-ConfGen/models/cnf_edge/odefunc.py pointer only (licence: NONE) · get_code("cd19b552f2be09f2") |
| divergence_approx | Not yet run | luost26/CGCF-ConfGen/models/cnf_edge/odefunc.py pointer only (licence: NONE) · get_code("58625622277c98f5") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We study how to generate molecule conformations (i.e., 3D structures) from a molecular graph. Traditional methods, such as molecular dynamics, sample conformations via computationally expensive simulations. Recently, machine learning methods have shown great potential by training on a large collection of conformation data. Challenges arise from the limited model capacity for capturing complex distributions of conformations and the difficulty in modeling long-range dependencies between atoms. Inspired by the recent progress in deep generative models, in this paper, we propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph. We propose a method combining the advantages of both flow-based and energy-based models, enjoying: (1) a high model capacity to estimate the multimodal conformation distribution; (2) explicitly capturing the complex long-range dependencies between atoms in the observation space. Extensive experiments demonstrate the superior performance of the proposed method on several benchmarks, including conformation generation and distance modeling tasks, with a significant improvement over existing generative models for molecular conformation sampling 1 . * Equal contribution. Work was done during Shitong's internship at Mila.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2102.10240")
get_code_for_paper("2102.10240")
have("2102.10240")
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