从头PROTAC设计中机器学习新兴方法的全面综述
A Comprehensive Review of Emerging Approaches in Machine Learning for De Novo PROTAC Design
AI总结：
该综述聚焦从头PROTAC设计领域，系统梳理了机器学习及基于片段的药物设计相关方法的应用进展，分析了现有研究的局限，为这一新型药物模态的设计策略优化提供参考。
摘要：
靶向蛋白降解（TPD）是现代药物发现中一个快速发展的领域，旨在通过利用细胞的固有降解途径选择性靶向并降解疾病相关蛋白，从而调控细胞内的蛋白水平。在基于占位效应的抑制剂未能取得成功的情况下，这一策略为治疗干预创造了新的机遇。蛋白水解靶向嵌合体（PROTACs）是TPD策略的核心，它利用泛素-蛋白酶体系统实现对致病蛋白的选择性靶向和蛋白酶体降解。随着该领域的发展，人们越来越清楚地认识到，设计此类复杂分子的传统方法存在局限性。这促使研究人员开始使用机器学习（ML）和生成式建模来改进并加速开发进程。在本综述中，我们探讨了机器学习对从头PROTAC设计的影响——尽管分子设计的这一方面意义重大，但尚未得到全面综述。我们深入研究了PROTAC连接子设计的独特特征，强调了构建能够实现TPD的有效双功能分子所需的复杂性。随后，我们考察了在小分子药物发现领域得到打磨的、基于片段的药物设计（FBDD）背景下的机器学习，如何为PROTAC连接子设计铺平道路。我们的综述对将该方法应用于复杂的PROTAC开发领域所固有的局限性进行了批判性评估。此外，我们综述了已有的应用于PROTAC设计的机器学习研究，重点介绍了开创性工作，以及更重要的是这些研究所面临的局限性。通过提供关于PROTAC开发现状以及机器学习在PROTAC设计中不可或缺的作用的见解，我们旨在为研究人员追求这一新模态的更佳设计策略提供有价值的视角。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
