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.

利用基于深度学习的分子生成加速药物候选物的发现的思想引起了极大关注，并开发了许多用于自动药物设计的深度生成模型，称为分子生成。其中，优化主导在实际药物设计中起着重要作用，我们对传统的计算机辅助药物设计方法进行了系统评述，将这些策略组织成了四个主要子任务，每个任务都有明确的输入和输出。此评述还探讨了基本概念、目标、传统 CADD 技术和 AIDD 的最新进展。此外，我们提出了一种基于受限子图生成的统一视角，以协调 de novo 设计和主导优化的方法。通过这种视角，de novo 设计可以融入主导优化的策略，以解决生成难以合成的分子的挑战；反之，主导优化可通过将其作为在特定次结构上生成分子的任务来受益于 de novo 设计的创新。