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.

翻译：基于深度学习的分子生成加速药物候选物发现的构想已引起广泛关注，许多深度生成模型被开发用于自动化药物设计，称为分子生成。通常，分子生成包含两种主要策略：全新设计（从头生成新型分子结构）和先导优化（将现有分子优化为药物候选物）。其中，先导优化在实际药物设计中扮演重要角色。例如，它可开发出与原药化学结构不同但疗效更优的“更优药物”，也能促进基于片段的药物设计，将虚拟筛选得到的低亲和力小配体转化为首创药物。尽管其重要性显著，但与成熟的全新生成模型相比，自动化先导优化因依赖复杂的生物学和化学知识而研究不足。为弥补这一差距，我们系统梳理了用于先导优化的传统计算方法，将这些策略归纳为四个具有明确输入输出的主要子任务。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技术及人工智能辅助药物设计的最新进展。此外，我们提出基于约束子图生成的统一视角，以协调全新设计与先导优化的方法论。通过这一视角，全新设计可借鉴先导优化策略应对难合成分子的生成挑战；反之，先导优化可将问题转化为基于特定子结构进行分子生成的任务，从而受益于全新设计的创新成果。