GenAIDD

Review papers

  1. Zhang, Zaixi, Jiaxian Yan, Yining Huang, Qi Liu, Enhong Chen, Mengdi Wang, and Marinka Zitnik. 2024. “Geometric Deep Learning for Structure-Based Drug Design: A Survey.” arXiv. https://doi.org/10.48550/arXiv.2306.11768.

  2. Tang, Xiangru, Howard Dai, Elizabeth Knight, Fang Wu, Yunyang Li, Tianxiao Li, and Mark Gerstein. 2024. “A Survey of Generative AI for de Novo Drug Design: New Frontiers in Molecule and Protein Generation.” Briefings in Bioinformatics 25 (4): bbae338. https://doi.org/10.1093/bib/bbae338.

Article

ArXiv, 2025, Molecular Text Generation, Discrete Diffusion

Lee, Seul, Karsten Kreis, Srimukh Prasad Veccham, Meng Liu, Danny Reidenbach, Yuxing Peng, Saee Paliwal, Weili Nie, and Arash Vahdat. 2025. “GenMol: A Drug Discovery Generalist with Discrete Diffusion.” arXiv. https://doi.org/10.48550/arXiv.2501.06158.

Nature Communications, 2024, Molecular Dynamics Trajectory Generation, Diffusion

Lu, Wei, Jixian Zhang, Weifeng Huang, Ziqiao Zhang, Xiangyu Jia, Zhenyu Wang, Leilei Shi, Chengtao Li, Peter G. Wolynes, and Shuangjia Zheng. “DynamicBind: Predicting Ligand-Specific Protein-Ligand Complex Structure with a Deep Equivariant Generative Model.” Nature Communications 15, no. 1 (February 5, 2024): 1071. https://doi.org/10.1038/s41467-024-45461-2.

Nature Computational Science, 2024, Structure-Based Drug Design, Equivariant Diffusion

Schneuing, Arne, Charles Harris, Yuanqi Du, Kieran Didi, Arian Jamasb, Ilia Igashov, Weitao Du, et al. 2024. “Structure-Based Drug Design with Equivariant Diffusion Models.” Nature Computational Science, December. https://doi.org/10.1038/s43588-024-00737-x.

Nature, 2023, de novo Protein Design, Diffusion

Watson, Joseph L., David Juergens, Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach, Woody Ahern, et al. 2023. “De Novo Design of Protein Structure and Function with RFdiffusion.” Nature 620 (7976): 1089–1100. https://doi.org/10.1038/s41586-023-06415-8.

NeurIPS, 2021, Molecular Graph Generation, GFlowNet

Bengio, Emmanuel, Moksh Jain, Maksym Korablyov, Doina Precup, and Yoshua Bengio. 2021. “Flow Network Based Generative Models for Non-Iterative Diverse Candidate Generation.” arXiv. http://arxiv.org/abs/2106.04399.

ArXiv, 2024, Structure-Based Drug Design, Target-Conditioned GFlowNet

Shen, Tony, Seonghwan Seo, Grayson Lee, Mohit Pandey, Jason R. Smith, Artem Cherkasov, Woo Youn Kim, and Martin Ester. 2024. “TacoGFN: Target-Conditioned GFlowNet for Structure-Based Drug Design.” arXiv. http://arxiv.org/abs/2310.03223.

ArXiv, 2024, Synthesizable Analog Generation, Transformer

Luo, Shitong, Wenhao Gao, Zuofan Wu, Jian Peng, Connor W. Coley, and Jianzhu Ma. 2024. “Projecting Molecules into Synthesizable Chemical Spaces.” arXiv. http://arxiv.org/abs/2406.04628.