  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.
