Advances in computational methods for PROTAC drug discovery

• PROTACs have attracted increasing interest but are challenging owing to size, polarity and flexibility. • Several PROTACs are in clinical trials but none has yet been approved. • Current ADMET models for small molecules perform poorly for PROTACs. • AI, ML, 3D descriptors and MD improve ADMET predictions. • Modern in silico tools with AI are advancing PROTAC development. Proteolysis-targeting chimeras (PROTACs) represent a transformative strategy in drug discovery, enabling the selective degradation of target proteins rather than merely inhibiting their activity. However, their structural complexity and deviation from conventional drug-like properties present major challenges for traditional design and optimization methods. In this review, we provide a comprehensive overview of recent computational advances that facilitate PROTAC development, encompassing chemoinformatics, structural bioinformatics, molecular modeling and machine learning resources. We highlight computational tools for warhead and linker design, ternary complex modeling and the prediction of degradation efficiency and ADMET profiles. Finally, we discuss current limitations and future perspectives, emphasizing strategies to enhance design effectiveness and accelerate clinical translation.