深度先导化合物优化：利用生成式人工智能进行结构修饰
Deep Lead Optimization: Leveraging Generative AI for Structural Modification
AI总结：
本文系统综述了基于深度学习的先导化合物优化方法，将其归纳为四个子任务，并提出基于约束子图生成的统一视角来融合从头设计与先导化合物优化，以推动自动化药物设计发展。
摘要：
利用基于深度学习的分子生成来加速药物候选物发现的想法已引起极大关注，许多深度生成模型已被开发用于自动化药物设计，称为分子生成。一般而言，分子生成包含两种主要策略：从头设计，即从零开始生成新颖的分子结构；以及先导化合物优化，即将现有分子精炼为药物候选物。其中，先导化合物优化在现实世界的药物设计中发挥着重要作用。例如，它能够开发出化学结构不同但比原研药更有效的me-better药物。它还可以促进基于片段的药物设计，将虚拟筛选出的低亲和力小配体转化为first-in-class药物。尽管其重要性显著，但与已较为成熟的从头生成模型相比，自动化先导化合物优化仍探索不足，这是因为它依赖于复杂的生物学和化学知识。为了弥合这一差距，我们对用于先导化合物优化的传统计算方法进行了系统性综述，将这些策略组织为四个具有明确输入和输出的主要子任务。本综述深入探讨了基本概念、目标、传统计算机辅助药物设计（CADD）技术以及人工智能驱动药物设计（AIDD）的最新进展。此外，我们引入了一种基于约束子图生成的统一视角，以协调从头设计和先导化合物优化的方法论。通过这一视角，从头设计可以吸收先导化合物优化的策略，以应对生成难以合成分子的挑战；反之，先导化合物优化可以通过将其视为以特定子结构为条件生成分子的任务，从而受益于从头设计的创新。
英文摘要：
The idea of using deep-learning-based molecular generation to accelerate discovery of drug candidates has attracted extraordinary attention, and many deep generative models have been developed for automated drug design, termed molecular generation. In general, molecular generation encompasses two main strategies: de novo design, which generates novel molecular structures from scratch, and lead optimization, which refines existing molecules into drug candidates. Among them, lead optimization plays an important role in real-world drug design. For example, it can enable the development of me-better drugs that are chemically distinct yet more effective than the original drugs. It can also facilitate fragment-based drug design, transforming virtual-screened small ligands with low affinity into first-in-class medicines. Despite its importance, automated lead optimization remains underexplored compared to the well-established de novo generative models, due to its reliance on complex biological and chemical knowledge. To bridge this gap, we conduct a systematic review of traditional computational methods for lead optimization, organizing these strategies into four principal sub-tasks with defined inputs and outputs. This review delves into the basic concepts, goals, conventional CADD techniques, and recent advancements in AIDD. Additionally, we introduce a unified perspective based on constrained subgraph generation to harmonize the methodologies of de novo design and lead optimization. Through this lens, de novo design can incorporate strategies from lead optimization to address the challenge of generating hard-to-synthesize molecules; inversely, lead optimization can benefit from the innovations in de novo design by approaching it as a task of generating molecules conditioned on certain substructures.
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。
