Chiralpedia

Complete Reference Library

Perspective & Editorials

  • Moores A, Zuin Zeidler VG. Don't let generative AI shape how we see chemistry. Nat Rev Chem. 2025;9(10):649-650.
    doi:10.1038/s41570-025-00757-9.

Reviews

  • Wigh DS, Goodman JM, Lapkin AA. A review of molecular representation in the age of machine learning. WIREs Comput Mol Sci. 2022;12(5):e1603.
    doi:10.1002/wcms.1603.
  • Cheng L, Shao PL, Zhao S, Zhang B, et al. Capturing stereochemical information with AI: Driving stereoselectivity prediction and rational design in asymmetric catalysis. Chin Chem Lett. 2026;37:113075.
    doi:10.1016/j.cclet.2026.113075.
  • Prabhakaran P, et al. Smart enantioseparation: A critical review of instrumentation, automation, and data-driven strategies in analytical chirality. Crit Rev Anal Chem. 2026.
    doi:10.1080/10408347.2026.2639581.
  • Nair R, et al. The significance of chirality in contemporary drug discovery: A mini review. RSC Adv. 2024.
    doi:10.1039/D4RA05694A.

Molecular Representation & Stereochemistry

  • Weininger D. SMILES, a chemical language and information system. I. Introduction to methodology and encoding rules. J Chem Inf Comput Sci. 1988;28(1):31-36.
    doi:10.1021/ci00057a005.
  • Krenn M, Häse F, Nigam A, Friederich P, Aspuru-Guzik A. SELFIES: A robust representation of semantically constrained graphs with an example application in chemistry. Mach Learn Sci Technol. 2020;1:045024.
    doi:10.1088/2632-2153/aba947.
  • Yoshikai Y, Mizuno T, Nemoto S, Kusuhara H. Difficulty in chirality recognition for Transformer architectures learning chemical structures from string representations. Nat Commun. 2024;15:1197.
    doi:10.1038/s41467-024-45102-8.
  • 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.
  • Papusha M, Leonhard K. StereoMolGraph: Stereochemistry-Aware Molecular and Reaction Graphs. J Chem Inf Model. 2026;66(7):3830-3839.
    doi:10.1021/acs.jcim.5c02523.

Chirality-Aware Machine Learning

  • Peng Y, Yu G, Shi R, Chen L, Wang X, Du W, Huo X, Yang Y. ChiralCat: Molecular chirality classification with enhanced spatial representation using learnable queries. Artif Intell Chem. 2025;3(2):100091.
    doi:10.1016/j.aichem.2025.100091.
  • Orsi M, Reymond JL. Assigning the stereochemistry of natural products by machine learning. J Cheminform. 2026;18:76.
    doi:10.1186/s13321-026-01205-6.

Graph Neural Networks & QSAR

  • . Gaiński P, Koziarski M, Tabor J, Śmieja M. ChiENN: Embracing Molecular Chirality with Graph Neural Networks. In: Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science. Cham: Springer; 2023.
    doi:10.1007/978-3-031-43418-1_3.
  • Liu Y, et al. Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure–Activity Relationship Modeling. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2023.
  • Yan J, et al. Interpretable Algorithm Framework of Molecular Chiral Graph Neural Network for QSAR Modeling. J Chem Inf Model. 2025.
    doi:10.1021/acs.jcim.4c02259.

Drug Discovery & Molecular Design

  • Sanchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: Generative models for matter engineering. Science. 2018;361(6400):360-365.
    doi:10.1126/science.aat2663.
  • Walters WP, Barzilay R. Applications of Deep Learning in Molecule Generation and Molecular Property Prediction. Acc Chem Res. 2021;54(2):263-270.
    doi:10.1021/acs.accounts.0c00699.
  • Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021;26(1):80-93.
    doi:10.1016/j.drudis.2020.10.010.
  • Srinivasan V, et al. Artificial intelligence in drug discovery: Recent advances and future perspectives. Expert Opin Drug Discov. 2021;16(8):949-959.
    doi:10.1080/17460441.2021.1909567.
  • Bharadwaj VS, et al. A review on machine learning approaches and trends in drug discovery. Comput Struct Biotechnol J. 2021;19:4538-4558.
    doi:10.1016/j.csbj.2021.08.011.
  • Kumar R, et al. A review on AI-enabled drug design in medicinal chemistry: Analytical validation, challenges, and regulatory considerations. Talanta. 2026.
    doi:10.1016/j.talanta.2026.129802.

Chiral Cliffs & Activity Cliffs

  • Schneider N, Lewis RA, Fechner N, Ertl P. Chiral Cliffs: Investigating the Influence of Chirality on Binding Affinity. ChemMedChem. 2018;13(13):1315-1324.
    doi:10.1002/cmdc.201700798.
  • van Tilborg D, Alenicheva A, Grisoni F. Exposing the Limitations of Molecular Machine Learning with Activity Cliffs. J Chem Inf Model. 2022;62(23):5938-5951.
    doi:10.1021/acs.jcim.2c01073.
  • Stumpfe D, Hu H, Bajorath J. Evolving Concept of Activity Cliffs. ACS Omega. 2019;4(11):14360-14368.
    doi:10.1021/acsomega.9b02221

Analytical Chirality & Enantioseparation

  • . Prabhakaran P, et al. Smart enantioseparation: A critical review of instrumentation, automation, and data-driven strategies in analytical chirality. Crit Rev Anal Chem. 2026.
    doi:10.1080/10408347.2026.2639581.
  • Chiral Chromatography and Artificial Intelligence Integration in Enantiomers Separation. Chirality. 2025;37(12):e70066.
    doi:10.1002/chir.70066.

Industrial Perspectives

  • Álvarez-Calero JM, Horwitz MA. Stereoselectivity Seems To Be the Hardest Word: Tracking the Evolution of Asymmetric API Syntheses from Medicinal Chemistry to Process Development. Synthesis. 2025;57(15):2261-2277.
    doi:10.1055/a-2603-0471.
  • Saini JPS, Thakur A, Yadav D. AI-driven innovations in pharmaceuticals: Optimizing drug discovery and industry operations. RSC Pharm. 2025;2:437-454.
    doi:10.1039/D4PM00323C.

Industry Commentary

  • Buntz B. How stereo-correct data can de-risk AI-driven drug discovery. Drug Discovery Trends. October 15, 2025.

Protein Structure Prediction

  • Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583-589.
    doi:10.1038/s41586-021-03819-2.
  • Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583-589.
    doi:10.1038/s41586-021-03819-2.

Books & Monographs

  • Ramsundar B, Eastman P, Walters P, Pande V. Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More. Sebastopol (CA): O'Reilly Media; 2019.