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AI tool streamlines drug synthesis

Researchers developed a machine-learning system that predicts how molecules form, cutting lab work time from months to days and reducing costs. The system uses asymmetric cross-coupling reactions to build complex compounds and can be applied across fields, deepening our understanding of chemistry.

SourceUniversity of Utah·JournalNature·TypeExperimental study·DateMar 9, 2026

Organocatalytic intramolecular enantioselective, atropselective, and diastereoselective macrocyclization of quinone methylidenes with alcohols

Researchers developed an organocatalytic method to create planar chiral cyclophanes with high stereoselectivity and diastereoselectivity. The method uses a chiral phosphoric acid catalyst and generates a reactive naphthoquinone methylene intermediate, leading to the formation of stable planar chiral type III cyclophanes.

SourceChinese Chemical Society·JournalCCS Chemistry·TypeExperimental study·DateOct 23, 2025

Simultaneous analysis of 21 chemical reactions... AI to transform new drug development​

Researchers developed a technology that precisely analyzes 21 types of reactants simultaneously using high-resolution fluorine nuclear magnetic resonance spectroscopy. This breakthrough contributes to new drug development and catalyst optimization in AI-driven autonomous synthesis.

SourceThe Korea Advanced Institute of Science and Technology (KAIST)·JournalJournal of the American Chemical Society·TypeObservational study·DateJun 19, 2025

Designing homochiral metal–organic frameworks with ultrahigh surface areas and stability for practical applications

A novel mixed-ligand strategy creates ultrahigh surface area, chemically stable chiral MOFs ideal for practical applications in asymmetric catalysis. The frameworks demonstrate record-breaking surface areas and exceptional structural features, making them suitable as heterogeneous catalysts.

SourceScience China Press·JournalScience Bulletin·TypeExperimental study·DateFeb 21, 2025

Robots and A.I. team up to discover highly selective catalysts

Researchers developed a machine learning model using advanced 2D chemical descriptors to predict highly selective asymmetric catalysts without quantum chemical computations. The model demonstrated high accuracy in predicting catalyst structures and selectivity, outperforming existing methods.

SourceHokkaido University·JournalAngewandte Chemie International Edition·TypeExperimental study·DateFeb 2, 2023