Yossra Gharbi and Rocío Mercado
Digital Discovery, 2024,3, 2158-2176
Abstract
Machine learning (ML) accelerates PROTAC design by optimizing linkers and protein–ligase interactions, enabling selective protein degradation for therapeutic applications, particularly targeting previously undruggable proteins.
Yu Chen, Fengyuan Liu, Samira Pal and Quanyin Hu
Chem. Soc. Rev., 2024,53, 9582-9608
From themed collection:
2024 Emerging Investigators
Abstract
This review proposes the concept of proteolysis-targeting drug delivery system (ProDDS), surveys the recent research in various ProDDSs, summarizes their design principles, and provides an outlook on future opportunities.
Yanyun Hong, Xiang Liu, Yinglong Li, Mengting Yu, Caolin Wang, Cunpeng Nie and Shan Xu
Chem. Commun., 2026,62, 4869-4888
Abstract
This schematic illustrates the multidimensional optimization strategies for BRD4-targeting PROTACs, encompassing the rational design of the ternary complex, diverse controllable activation systems, and targeted delivery approaches.
Toshihiko Tashima
RSC Med. Chem., 2026, Advance Article
Abstract
CD36-mediated endocytosis provides an alternative uptake pathway for bRo5 PROTACs beyond passive diffusion.
Nils Dunlop, Francisco Erazo, Farzaneh Jalalypour and Rocío Mercado
Digital Discovery, 2025,4, 3782-3809
Abstract
Accurate prediction of protein–ligand and protein–protein interactions is essential for computational drug discovery, yet remains a significant challenge, particularly for complexes involving large, flexible ligands.