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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.
This section should list any major frameworks that you built your project using. Leave any add-ons/plugins for the acknowledgements section. Here are a few examples.
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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
Distributed under the MIT License. See LICENSE for more information.
- Yogesh Kalakoti - @iam_ysk - [email protected]
- Shashank Yadav - @xinformatics - [email protected]
Project Link: https://github.com/your_username/repo_name