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license: mit |
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--- |
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# Timewarp datasets |
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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. |
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Please see the [accompanying GitHub repository](https://github.com/microsoft/timewarp). |
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This dataset consists of many molecular dynamics trajectories of small peptides (2-4 amino acids) simulated with an implicit water force field. |
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For each protein two files are available: |
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* `protein-state0.pdb`: contains the topology and initial 3D XYZ coordinates. |
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* `protein-arrays.npz`: contains trajectory information. |
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The datasets are are split into the following directories: |
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# 2AA-1-big "Two Amino Acid" data set |
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This folder contains a data set of all-atom molecular dynamics trajectories for 380 |
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of the 400 dipeptides, i.e. small proteins composed of two amino acids. |
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This dataset was orginally created missing 20 of the 400 possible dipeptides. |
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The `2AA-1-complete` dataset completes this by including all 400. |
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Each peptide is simulated using classical molecular dynamics and the |
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water is simulated using an implicit water model. |
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The trajectories are only saved every 10000 MD steps. There is no intermediate |
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spacing as for the other datasets for the Timewarp project. |
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# 2AA-1-complete "Two Amino Acid" data set |
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This folder contains a data set of all-atom molecular dynamics trajectories for all 400 |
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dipeptides, i.e. small proteins composed of two amino acids. |
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This includes also the peptides missing in the other 2AA datasets. |
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Each peptide is simulated using classical molecular dynamics and the |
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water is simulated using an implicit water model. |
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# 4AA-huge "Four Amino Acid" data set, tetrapeptides |
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This folder contains a data set of all-atom molecular dynamics trajectories for |
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tetrapeptides, i.e. small proteins composed of four amino acids. |
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The data set contains mostly validation and test trajectories as it was mostly |
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used to validation and test purposes. |
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The training trajectories used are usually shorter. |
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Each peptide is simulated for 1 micro second using classical molecular dynamics and the |
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water is simulated using an implicit water model. |
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# 4AA-large "Four Amino Acid" data set, tetrapeptides |
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This folder contains a data set of all-atom molecular dynamics trajectories for |
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2333 tetrapeptides, i.e. small proteins composed of four amino acids. |
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The data set is split into 1500 tetra-peptides in the train set, 400 in validation, and 433 in test. |
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Each peptide in the train set is simulated for 50ns using classical molecular dynamics and the |
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water is simulated using an implicit water model. Each other peptide is simulated for 500ns. |
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## Responsible AI FAQ |
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- What is Timewarp? |
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- 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. |
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- What can Timewarp do? |
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- Timewarp can be used to sample from the equilibrium distribution of small peptides. |
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- What is/are Timewarp’s intended use(s)? |
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- Timewarp is intended for machine learning and molecular dynamics research purposes only. |
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- How was Timewarp evaluated? What metrics are used to measure performance? |
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- 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. |
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- What are the limitations of Timewarp? How can users minimize the impact of Timewarp’s limitations when using the system? |
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- 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. |
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- What operational factors and settings allow for effective and responsible use of Timewarp? |
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- Timewarp should be used purely for research purposes only. |
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## Contributing |
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This project welcomes contributions and suggestions. Most contributions require you to agree to a |
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Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us |
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the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. |
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When you submit a pull request, a CLA bot will automatically determine whether you need to provide |
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a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions |
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provided by the bot. You will only need to do this once across all repos using our CLA. |
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). |
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For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or |
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contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. |
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## Trademarks |
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This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft |
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trademarks or logos is subject to and must follow |
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[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general). |
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Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. |
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Any use of third-party trademarks or logos are subject to those third-party's policies. |
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