Readers beginning their journey into stereochemistry-aware artificial intelligence are
encouraged to explore the following publications in the recommended sequence.
Foundations of
Molecular Representation
- Wigh DS, Goodman JM, Lapkin AA. A review of molecular representation in the age of
machine learning. WIREs Computational Molecular Science. 2022;12(5):e1603.
doi:10.1002/wcms.1603
Understanding Current
Limitations
- Yoshikai Y, Mizuno T, Nemoto S, Kusuhara H. Difficulty in chirality recognition for
Transformer architectures learning chemical structures from string representations.
Nature Communications. 2024;15:1197. doi:10.1038/s41467-024-45102-8
Stereo-aware Molecular
Generation
- Tom G, Yu E, Yoshikawa N, Jorner K, Aspuru-Guzik A. Stereochemistry-aware
string-based molecular generation. PNAS Nexus. 2025;4(11):pgaf329.
doi:10.1093/pnasnexus/pgaf329
Chirality-aware Machine
Learning
- Peng Y, Yu G, Shi R, et al. ChiralCat: Molecular chirality classification with
enhanced spatial representation using learnable queries. Artificial Intelligence
Chemistry. 2025;3:100091. doi:10.1016/j.aichem.2025.100091
Graph Neural Networks
- Gaiński P, Koziarski M, Tabor J, Śmieja M. ChiENN: Embracing Molecular Chirality
with Graph Neural Networks. Machine Learning and Knowledge Discovery in Databases
(Lecture Notes in Computer Science). Springer; 2023. doi:10.1007/978-3-031-43418-1_3
Advanced Molecular
Graph Representations
- Papusha M, Leonhard K. StereoMolGraph: Stereochemistry-Aware Molecular and
Reaction
Graphs. Journal of Chemical Information and Modeling. 2026;66(7):3830–3839.
doi:10.1021/acs.jcim.5c02523
AI for Asymmetric
Catalysis
- Cheng L, Shao PL, Zhao S, et al. Capturing stereochemical information with AI:
Driving stereoselectivity prediction and rational design in asymmetric catalysis.
Chinese Chemical Letters. 2026;37:113075. doi:10.1016/j.cclet.2026.113075
Natural Products
- 8. Orsi M, Reymond JL. Assigning the stereochemistry of natural products by
machine
learning. Journal of Cheminformatics. 2026;18:76. doi:10.1186/s13321-026-01205-6
Perspectives on AI and
Chemistry
- Moores A, Zuin Zeidler VG. Don't let generative AI shape how we see chemistry.
Nature Reviews Chemistry. 2025;9(10):649–650. doi:10.1038/s41570-025-00757-9
Broader Applications in
Drug Discovery
- Walters WP, Barzilay R. Applications of Deep Learning in Molecule Generation and
Molecular Property Prediction. Accounts of Chemical Research. 2021;54(2):263–270.
doi:10.1021/acs.accounts.0c00699
Note on the Sequence of
Arrangement:
The publications are arranged in a recommended learning sequence, progressing from
foundational concepts and current limitations to advanced methodologies, specialized
applications, and broader perspectives in stereochemistry-aware AI.
