DeepCRE: Transforming Drug R&D via AI-Driven Cross-drug Response Evaluation
<|reference_start|>DeepCRE: Transforming Drug R&D via AI-Driven Cross-drug Response Evaluation: The fields of therapeutic application and drug research and development (R&D) both face substantial challenges, i.e., the therapeutic domain calls for more treatment alternatives, while numerous promising pre-clinical drugs have failed in clinical trials. One of the reasons is the inadequacy of Cross-drug Response Evaluation (CRE) during the late stages of drug R&D. Although in-silico CRE models bring a promising solution, existing methodologies are restricted to early stages of drug R&D, such as target and cell-line levels, offering limited improvement to clinical success rates. Herein, we introduce DeepCRE, a pioneering AI model designed to predict CRE effectively in the late stages of drug R&D. DeepCRE outperforms the existing best models by achieving an average performance improvement of 17.7% in patient-level CRE, and a 5-fold increase in indication-level CRE, facilitating more accurate personalized treatment predictions and better pharmaceutical value assessment for indications, respectively. Furthermore, DeepCRE has identified a set of six drug candidates that show significantly greater effectiveness than a comparator set of two approved drugs in 5/8 colorectal cancer organoids. This demonstrates the capability of DeepCRE to systematically uncover a spectrum of drug candidates with enhanced therapeutic effects, highlighting its potential to transform drug R&D.<|reference_end|>
@article{wu2024deepcre:,
    title={DeepCRE: Transforming Drug R&D via AI-Driven Cross-drug Response
  Evaluation},
    author={Yushuai Wu, Ting Zhang, Hao Zhou, Hainan Wu, Hanwen Sunchu, Lei Hu,
  Xiaofang Chen, Suyuan Zhao, Gaochao Liu, Chao Sun, Jiahuan Zhang, Yizhen Luo,
  Peng Liu, Zaiqing Nie and Yushuai Wu},
    journal={arXiv preprint arXiv:2403.03768},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03768},
    primaryClass={cs.AI cs.LG q-bio.QM}
}