andrewfoong
commited on
Commit
•
04ef754
1
Parent(s):
d2e9d05
Update README.md
Browse files
README.md
CHANGED
@@ -4,7 +4,8 @@ license: mit
|
|
4 |
|
5 |
# Timewarp datasets
|
6 |
|
7 |
-
This dataset contains molecular dynamics simulation data that was used to train the neural networks in the NeurIPS 2023 paper [Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics](https://arxiv.org/abs/2302.01170).
|
|
|
8 |
|
9 |
This dataset consists of many molecular dynamics trajectories of small peptides (2-4 amino acids) simulated with an implicit water force field.
|
10 |
For each protein two files are available:
|
@@ -50,3 +51,40 @@ This folder contains a data set of all-atom molecular dynamics trajectories for
|
|
50 |
The data set is split into 1500 tetra-peptides in the train set, 400 in validation, and 433 in test.
|
51 |
Each peptide in the train set is simulated for 50ns using classical molecular dynamics and the
|
52 |
water is simulated using an implicit water model. Each other peptide is simulated for 500ns.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
4 |
|
5 |
# Timewarp datasets
|
6 |
|
7 |
+
This dataset contains molecular dynamics simulation data that was used to train the neural networks in the NeurIPS 2023 paper [Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics](https://arxiv.org/abs/2302.01170) by Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, and Ryota Tomioka.
|
8 |
+
Please see the [accompanying GitHub repository](https://github.com/microsoft/timewarp).
|
9 |
|
10 |
This dataset consists of many molecular dynamics trajectories of small peptides (2-4 amino acids) simulated with an implicit water force field.
|
11 |
For each protein two files are available:
|
|
|
51 |
The data set is split into 1500 tetra-peptides in the train set, 400 in validation, and 433 in test.
|
52 |
Each peptide in the train set is simulated for 50ns using classical molecular dynamics and the
|
53 |
water is simulated using an implicit water model. Each other peptide is simulated for 500ns.
|
54 |
+
|
55 |
+
## Responsible AI FAQ
|
56 |
+
- What is Timewarp?
|
57 |
+
- Timewarp is a neural network that predicts the future 3D positions of a small peptide (2- 4 amino acids) based on its current state. It is a research project that investigates using deep learning to accelerate molecular dynamics simulations.
|
58 |
+
- What can Timewarp do?
|
59 |
+
- Timewarp can be used to sample from the equilibrium distribution of small peptides.
|
60 |
+
- What is/are Timewarp’s intended use(s)?
|
61 |
+
- Timewarp is intended for machine learning and molecular dynamics research purposes only.
|
62 |
+
- How was Timewarp evaluated? What metrics are used to measure performance?
|
63 |
+
- Timewarp was evaluated by comparing the speed of molecular dynamics sampling with standard molecular dynamics systems that rely on numerical integration. Timewarp is sometimes faster than these standard systems.
|
64 |
+
- What are the limitations of Timewarp? How can users minimize the impact of Timewarp’s limitations when using the system?
|
65 |
+
- As a research project, Timewarp has many limitations. The main ones are that it only works for very small peptides (2-4 amino acids), and that it does not lead to a wall-clock speed up for many peptides.
|
66 |
+
- What operational factors and settings allow for effective and responsible use of Timewarp?
|
67 |
+
- Timewarp should be used purely for research purposes only.
|
68 |
+
|
69 |
+
|
70 |
+
## Contributing
|
71 |
+
|
72 |
+
This project welcomes contributions and suggestions. Most contributions require you to agree to a
|
73 |
+
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
|
74 |
+
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
|
75 |
+
|
76 |
+
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
|
77 |
+
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
|
78 |
+
provided by the bot. You will only need to do this once across all repos using our CLA.
|
79 |
+
|
80 |
+
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
|
81 |
+
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
|
82 |
+
contact [[email protected]](mailto:[email protected]) with any additional questions or comments.
|
83 |
+
|
84 |
+
## Trademarks
|
85 |
+
|
86 |
+
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
|
87 |
+
trademarks or logos is subject to and must follow
|
88 |
+
[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
|
89 |
+
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
|
90 |
+
Any use of third-party trademarks or logos are subject to those third-party's policies.
|