Abstract
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.
Abstract (translated)
使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引起了非凡的关注，并为自动药物设计开发了许多深度生成模型，称为分子生成。通常，分子生成包括两种主要策略：从头设计（从零生成新分子结构）和lead优化（优化现有分子以成为药物候选人）。在它们中，lead优化在现实世界的药物设计中扮演着重要的角色。例如，它可以通过生成化学上与原始药物不同的但更有效的更好药物来开发。它还可以促进基于片段的药物设计，将具有低亲和力的虚拟筛选的小分子转化为第一类药物。尽管它在药物设计中具有重要地位，但与经过充分验证的从头生成模型相比，自动lead优化仍然缺乏研究，原因是它依赖于复杂的生物和化学知识。为了弥合这一差距，我们进行了一项系统性的回顾，回顾了传统计算方法在lead优化中的作用，将这些策略组织成四个明确定义的子任务。本审查深入探讨了基本概念、目标、传统的CADD技术和AIDD中 recent advances。此外，我们还引入了一个基于约束子图生成的统一视角，以协调从头设计和解lead优化的方法。通过这个视角，从头设计可以吸收来自lead优化的策略来解决生成难以合成化合物的挑战；相反，lead优化可以从头设计的创新中受益，将其视为生成一定子结构分子的任务。
URL
https://arxiv.org/abs/2404.19230
