The optimal design of compounds through manipulating properties at the molecular level is often the key to considerable scientific advances and improved process systems performance. This paper highlights key trends, challenges, and opportunities underpinning the Computer-Aided Molecular Design (CAMD) problems. A brief review of knowledge-driven property estimation methods and solution techniques, as well as corresponding CAMD tools and applications, are first presented. In view of the computational challenges plaguing knowledge-based methods and techniques, we survey the current state-of-the-art applications of deep learning to molecular design as a fertile approach towards overcoming computational limitations and navigating uncharted territories of the chemical space. The main focus of the survey is given to deep generative modeling of molecules under various deep learning architectures and different molecular representations. Further, the importance of benchmarking and empirical rigor in building deep learning models is spotlighted. The review article also presents a detailed discussion of the current perspectives and challenges of knowledge-based and data-driven CAMD and identifies key areas for future research directions. Special emphasis is on the fertile avenue of hybrid modeling paradigm, in which deep learning approaches are exploited while leveraging the accumulated wealth of knowledge-driven CAMD methods and tools.

通过在分子水平上控制性能来优化化合物的设计通常是取得重大科学进展和改善工艺系统性能的关键。本文重点介绍了计算机辅助分子设计（CAMD）问题的主要趋势，挑战和机遇。首先简要介绍了知识驱动的属性估计方法和解决方案技术，以及相应的CAMD工具和应用程序。鉴于困扰基于知识的方法和技术的计算挑战，我们调查了深度学习在分子设计方面的最新应用，以此作为克服计算限制和导航化学空间未知领域的沃土方法。该调查的主要重点是在各种深度学习架构和不同分子表示形式下的分子的深度生成模型。此外，基准测试和经验严谨性在构建深度学习模型中的重要性也得到了关注。这篇综述文章还对基于知识和数据驱动的CAMD的当前观点和挑战进行了详细讨论，并确定了未来研究方向的关键领域。特别强调的是混合建模范例的沃土之路，在其中利用深度学习方法，同时利用积累的大量知识驱动的CAMD方法和工具。这篇综述文章还对基于知识和数据驱动的CAMD的当前观点和挑战进行了详细讨论，并确定了未来研究方向的关键领域。特别强调的是混合建模范例的沃土之路，在其中利用深度学习方法，同时利用积累的大量知识驱动的CAMD方法和工具。这篇综述文章还对基于知识和数据驱动的CAMD的当前观点和挑战进行了详细讨论，并确定了未来研究方向的关键领域。特别强调的是混合建模范例的沃土之路，在其中利用深度学习方法，同时利用积累的大量知识驱动的CAMD方法和工具。