干草堆
广告
药代动力学
药效学
药理学
医学
计算机科学
万维网
作者
Giulia Apprato,Giulia Caron,Gauri Deshmukh,Diego J. Jiménez,Robin Thomas Ulrich Haid,Andy Pike,Andreas Reichel,Caroline Rynn,Zhang Donglu,Matthias Wittwer
标识
DOI:10.1080/17460441.2025.2467195
摘要
Despite many recent advances in the field, continued research will further our understanding of rational design regarding degrader optimization. Machine-learning and computational approaches will become increasingly important once larger, more robust datasets become available. Furthermore, tissue-targeting approaches (particularly regarding the Central Nervous System will be increasingly studied to elucidate efficacious drug regimens that capitalize on the catalytic mode of action. Finally, additional specialized approaches (e.g. covalent degraders, LOVdegs) can enrich the field further and offer interesting alternative approaches.
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