Skeletal editing enables atom-level modifications to molecular skeletons. This review highlights advances in single-atom insertions, deletions, and transmutations in (hetero)cyclic systems, offering a comprehensive view on cutting-edge strategies.

Dehydrohelicenes are some of the most attractive chiroptical materials with unique helical chirality. However, to our knowledge, there are no prior reports on their direct construction by asymmetric methods. In this work, sequential synthesis of aza-oxa-dehydro[7]helicenes via the electrochemical oxidative hetero-coupling of 3-hydoxycarbazoles and 2-naphthols followed by dehydrative cyclization and intramolecular C–C bond formation has been realized. In addition, an efficient enantioselective synthesis through chiral vanadium-catalyzed hetero-coupling and electrochemical oxidative transformations afforded heterodehydro[7]helicene without any racemization. The obtained dehydro[7]helicenes showed intense blue-colored circularly polarized luminescence (|g_lum| ≈ 2.5 × 10⁻³ at 433 nm). Thermodynamic and kinetic studies of the racemization barrier of heterodehydro[7]helicenes indicated significant chiral stability with ΔG^‡> 140 kJ mol⁻¹.

Protein kinases are central regulators of cell signaling and play pivotal roles in a wide array of diseases, most notably cancer and autoimmune disorders. The clinical success of kinase inhibitors—such as imatinib and osimertinib—has firmly established kinases as valuable drug targets. However, the development of selective, potent inhibitors remains challenging due to the conserved nature of the ATP-binding site, off-target effects, resistance mutations, and patient-specific variability. Recent advances in artificial intelligence (AI) and machine learning (ML) offer transformative solutions to these obstacles across the drug discovery pipeline. This review explores how AI/ML methods, including deep learning, graph neural networks, and generative models, are revolutionizing the design, optimization, and repurposing of kinase inhibitors. We detail applications in target identification, virtual screening, structure–activity relationship modeling, resistance prediction, and clinical trial design. Representative case studies—such as AI-optimized BTK and EGFR inhibitors—highlight real-world impact. We also examine current limitations, including data sparsity, model interpretability, and translational gaps between in silico and experimental results. Finally, we discuss emerging directions such as federated learning, personalized kinase inhibitors, and AI-enabled combination therapies. By integrating computational innovation with medicinal chemistry, AI/ML holds immense promise to accelerate and refine the next generation of kinase-targeted therapeutics.

Autoxidation of aldehydes to peracids and carboxylic acids holds a significant impact in both academia and industry due to their wide applications in organic synthesis and environmental remediation. However, the multiple pathways involved in this reaction have hindered the development of sustainable methods for peracid synthesis. Herein, we conduct a comprehensive kinetic and mechanistic investigation to elucidate the interplay between these pathways. Subsequently, we introduce an efficient eco-friendly method to oxidize aldehydes to their corresponding peracids under sunlight or UV irradiation using oxygen as the sole oxidant without any additives or photocatalysts. Additionally, we demonstrate a simple method for the autoxidation of aldehydes to carboxylic acids via controlling key parameters such as the wavelength and solvent. These methods exhibit broad applicability to aromatic and aliphatic aldehydes, successful scaling up to the gram scale, and utilizing renewable solar energy making them good alternatives for traditional methods.