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.

以机器学习为基础，探讨了 ML 在新型靶向蛋白降解工具 (PROTAC) 设计中的应用、影响及局限性。

机器学习在全新 PROTAC 设计中新兴方法的综述

A Comprehensive Review of Emerging Approaches in Machine Learning for De  Novo PROTAC Design

Proteolysis-Targeting Chimeras (PROTACs) represent a novel class of small
molecules which are designed to act as a bridge between an E3 ligase and a
disease-relevant protein, thereby promoting its subsequent degradation. PROTACs
are composed of two protein binding "active" domains, linked by a "linker"
domain. The design of the linker domain is challenging due to geometric and
chemical constraints given by its interactions, and the need to maximize
drug-likeness. To tackle these challenges, we introduce ShapeLinker, a method
for de novo design of linkers. It performs fragment-linking using reinforcement
learning on an autoregressive SMILES generator. The method optimizes for a
composite score combining relevant physicochemical properties and a novel,
attention-based point cloud alignment score. This new method successfully
generates linkers that satisfy both relevant 2D and 3D requirements, and
achieves state-of-the-art results in producing novel linkers assuming a target
linker conformation. This allows for more rational and efficient PROTAC design
and optimization. Code and data are available at
this https URL

提出了一种新方法 ShapeLinker，该方法利用增强学习，优化物理化学属性和基于注意力的点云对齐评分，成功地生成了既满足 2D 和 3D 要求，同时又符合药物相似性的独特的连接器，从而更加合理和高效地进行 PROTAC 的设计和优化。