  Targeted protein degradation (TPD) is a rapidly growing field in modern drug
discovery that aims to regulate the intracellular levels of proteins by
harnessing the cell's innate degradation pathways to selectively target and
degrade disease-related proteins. This strategy creates new opportunities for
therapeutic intervention in cases where occupancy-based inhibitors have not
been successful. Proteolysis-targeting chimeras (PROTACs) are at the heart of
TPD strategies, which leverage the ubiquitin-proteasome system for the
selective targeting and proteasomal degradation of pathogenic proteins. As the
field evolves, it becomes increasingly apparent that the traditional
methodologies for designing such complex molecules have limitations. This has
led to the use of machine learning (ML) and generative modeling to improve and
accelerate the development process. In this review, we explore the impact of ML
on de novo PROTAC design $-$ an aspect of molecular design that has not been
comprehensively reviewed despite its significance. We delve into the distinct
characteristics of PROTAC linker design, underscoring the complexities required
to create effective bifunctional molecules capable of TPD. We then examine how
ML in the context of fragment-based drug design (FBDD), honed in the realm of
small-molecule drug discovery, is paving the way for PROTAC linker design. Our
review provides a critical evaluation of the limitations inherent in applying
this method to the complex field of PROTAC development. Moreover, we review
existing ML works applied to PROTAC design, highlighting pioneering efforts
and, importantly, the limitations these studies face. By offering insights into
the current state of PROTAC development and the integral role of ML in PROTAC
design, we aim to provide valuable perspectives for researchers in their
pursuit of better design strategies for this new modality.
