Guang Yang, Jianing Li, Qiong Wang, Lianlian Wu, Zhengshan Chen, Peng-Cheng Zhao, Haoyang Wang, Qinglong Wang, Bing Wen, Chao Song, Wenxin Xu, Xiongfei He, Xiao Zhang, Sophia Tsoka, Siu-Ming Yiu, Fang‐Xiang Wu, Meng Xiao, Shirui Pan, Hui Yu, Song He, Xiaoli Li, Xiaochen Bo, Min Wu, Jian‐Yu Shi
Artificial Intelligence (AI), particularly Generative AI (GenAI), has revolutionized molecule design by enabling the creation of novel and complex molecular structures, advancing drug discovery, materials science, and synthetic biology. This survey provides a comprehensive exploration of AI-driven molecule design, categorized within and beyond the central dogma of molecular biology. We first introduce the foundational concepts of GenAI, focusing on architectures such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models (DMs), which underpin molecular design applications. Then, we examine how GenAI facilitates molecule design within the central dogma, addressing DNA, RNA, and protein design. Beyond the central dogma, we explore applications in designing small molecules, lipids, glycans, and other biomaterials. Finally, we conclude by discussing current challenges, emerging trends, and potential future directions for GenAI in molecule design. Overall, this survey aims to equip researchers with a thorough understanding of this rapidly-evolving field and inspire novel developments and breakthroughs in GenAI for molecule design.