TransDTI
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About
The identification of novel drug-target interactions is a labor-intensive and low throughput process. In silico alternatives have proved to be of immense importance in assisting the drug discovery process. Here, we present TransDTI, a multi-class classification and regression workflow employing transformer-based language models to segregates interactions between drug-target pairs as active, inactive and intermediate.
Built With
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Usage
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For more examples, please refer to the Documentation
Contributing
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
License
Distributed under the MIT License. See LICENSE for more information.
Contact
- Yogesh Kalakoti - @iam_ysk - yogesh.kalakoti@dbeb.iitd.ac.in
- Shashank Yadav - @xinformatics - shashank.yadav1@alumni.iitd.ac.in
Project Link: https://github.com/your_username/repo_name