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## Getting started
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
[chembl](https://www.ebi.ac.uk/chembl/)
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
DOI https://doi.org/10.1016/j.ejmech.2022.114495
## Add your files
Design, synthesis and activity against drug-resistant bacteria evaluation of C-20, C-23 modified 5-O-mycaminosyltylonolide derivatives
- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
A类似物：22个活性数据
B类似物：7个活性数据
C类似物：47个活性数据
```
cd existing_repo
git remote add origin http://home.jmsu.top/lingyuzeng/qsar.git
git branch -M main
git push -uf origin main
检索条件：Structure2D_A1.mol 85% 以上相似度
检索结果：A_85.csv
## env
[unimol install](https://unimol.readthedocs.io/en/latest/installation.html#install)
```shell
python -m pip install --upgrade pip
pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
pip install unimol_tools
pip install huggingface_hub
```
## Integrate with your tools
- [ ] [Set up project integrations](http://home.jmsu.top/lingyuzeng/qsar/-/settings/integrations)
## result
## Collaborate with your team
- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
## Test and Deploy
Use the built-in continuous integration in GitLab.
- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
***
# Editing this README
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
## Suggestions for a good README
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
## Name
Choose a self-explaining name for your project.
## Description
Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
## Badges
On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
## Visuals
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
## Installation
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
## Usage
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
## Support
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
## Roadmap
If you have ideas for releases in the future, it is a good idea to list them in the README.
## Contributing
State if you are open to contributions and what your requirements are for accepting them.
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
## Authors and acknowledgment
Show your appreciation to those who have contributed to the project.
## License
For open source projects, say how it is licensed.
## Project status
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
```shell
(analyse) (base) root@DESK4090:/mnt/c/project/qsar/MIC# python qsar_1D.py
[1D-QSAR][Linear Regression] MSE:32.3949 R2:0.6525
Model saved to 1d_qsar_linear_regression_model.pkl
[1D-QSAR][Stochastic Gradient Descent] MSE:230009980374197965960989638656.0000 R2:-2467672699617844819673481216.0000
Model saved to 1d_qsar_stochastic_gradient_descent_model.pkl
[1D-QSAR][K-Nearest Neighbors] MSE:30.2081 R2:0.6759
Model saved to 1d_qsar_k-nearest_neighbors_model.pkl
[1D-QSAR][Decision Tree] MSE:27.7150 R2:0.7027
Model saved to 1d_qsar_decision_tree_model.pkl
[1D-QSAR][Random Forest] MSE:26.5204 R2:0.7155
Model saved to 1d_qsar_random_forest_model.pkl
[1D-QSAR][XGBoost] MSE:27.7147 R2:0.7027
Model saved to 1d_qsar_xgboost_model.pkl
[1D-QSAR][Multi-layer Perceptron] MSE:143.3505 R2:-0.5379
Model saved to 1d_qsar_multi-layer_perceptron_model.pkl
---
[2D-QSAR][Linear Regression] MSE:30.1093 R2:0.6770
Model saved to 2d_qsar_linear_regression_model.pkl
[2D-QSAR][Stochastic Gradient Descent] MSE:33.7336 R2:0.6381
Model saved to 2d_qsar_stochastic_gradient_descent_model.pkl
[2D-QSAR][K-Nearest Neighbors] MSE:48.8179 R2:0.4763
Model saved to 2d_qsar_k-nearest_neighbors_model.pkl
[2D-QSAR][Decision Tree] MSE:30.2360 R2:0.6756
Model saved to 2d_qsar_decision_tree_model.pkl
[2D-QSAR][Random Forest] MSE:28.7916 R2:0.6911
Model saved to 2d_qsar_random_forest_model.pkl
[2D-QSAR][XGBoost] MSE:30.2351 R2:0.6756
Model saved to 2d_qsar_xgboost_model.pkl
[2D-QSAR][Multi-layer Perceptron] MSE:30.1715 R2:0.6763
Model saved to 2d_qsar_multi-layer_perceptron_model.pkl
---
[3D-QSAR][Stochastic Gradient Descent] MSE:64.5768 R2:0.3072
Model saved to 3d_qsar_stochastic_gradient_descent_model.pkl
[3D-QSAR][K-Nearest Neighbors] MSE:38.6921 R2:0.5849
Model saved to 3d_qsar_k-nearest_neighbors_model.pkl
[3D-QSAR][Decision Tree] MSE:30.2360 R2:0.6756
Model saved to 3d_qsar_decision_tree_model.pkl
[3D-QSAR][Random Forest] MSE:30.8310 R2:0.6692
Model saved to 3d_qsar_random_forest_model.pkl
[3D-QSAR][XGBoost] MSE:30.2362 R2:0.6756
Model saved to 3d_qsar_xgboost_model.pkl
[3D-QSAR][Multi-layer Perceptron] MSE:29.9844 R2:0.6783
Model saved to 3d_qsar_multi-layer_perceptron_model.pkl
---
unimol qsar
{'mse': 59.72037918598548, 'mae': 5.179289798987539, 'pearsonr': 0.638764928149331, 'spearmanr': 0.6006870492749102, 'r2': 0.35928715315601223}
```