Computational approaches to drug resistance prediction are of great interest for the development of new drugs in order to overcome mutation-induced drug resistance. Here, the authors develop a state-of-the-art database, MdrDB, which integrates data from seven publicly available datasets, expands the existing drug resistance data, and enhances the performance of classical machine learning models for drug resistance prediction.
- Ziyi Yang
- Zhaofeng Ye
- Shengyu Zhang
