GitHub Repository
Created On
Updated On
An ML-driven application for drug discovery in breast cancer utilizes machine learning algorithms to predict the pIC50 values of chemical compounds. By analyzing the relationships between compound structures and their biological activities against breast cancer, the application can provide valuable insights into the efficacy of potential drug candidates.

More details

Use Cases Limitations Evidence Owner's Insight

By leveraging molecular features and descriptors, the application assists researchers in identifying promising compounds, accelerating the development of effective treatments for breast cancer.

✔️ Accelerating Progress through Machine Learning ✔️ Machine learning-driven application predicts pIC50 values of chemical compounds. ✔️ Identification of potential drug candidates for breast cancer. ✔️ Analysis of compound structures and their relationships with biological activities. ✔️ Valuable insights into the efficacy of drug candidates. ✔️ Accelerates the identification and development of promising compounds. ✔️ Significant contribution to the battle against breast cancer. ✔️ Potential to transform patient outcomes and bring new hope for the future.

See owner's GitHub Repo for more info:

See owner's GitHub Repo for more info:

This application was not uploaded by the author, but through their publicly available Github repository (

MIT License

Copyright (c) 2023 Daniel G. Endalamaw S.


Warning: Not intended for clinical use. Assume outputs are unsafe and unvalidated. Use carefully.

  • Favorites: 1
  • Executions: 33

  • Drug Discovery & Development



Member since