The exploration of inexpensive and stable heterogeneous catalysts for C–S coupling reactions remains a challenging issue. Herein, we successfully prepared a new biomass-derived copper catalyst and applied it to the selective C(sp³)–H-directed sulfonylation of ketone hydrazones with commercial sodium sulfinates. The catalyst was characterized using different spectroscopic and microscopic techniques. Importantly, the prepared biomass-supported Cu catalyst catalyzed the C–S coupling reaction with considerably high activity. A variety of aryl and alkyl sulfinates were converted to the corresponding sulfones with good yields. In particular, the heterogeneous catalyst could be recovered easily after the synthesis and consecutively used at least five times with no appreciable decrease in the catalytic activity. Lastly, the copper catalyst is expected to have further applications in organic reactions catalyzed by Cu/CuO₂.

The eEF2K, a member of the α-kinase family, plays a crucial role in cellular differentiation and the stability of the nervous system. The development of eEF2K inhibitors has proven to be significantly important in the treatment of diseases such as cancer and Alzheimer’s. With the advancement of big data in pharmaceuticals and the evolution of molecular generation technologies, leveraging artificial intelligence to expedite research on eEF2K inhibitors shows great potential. Based on the recently published structure of eEF2K and known inhibitor molecular structures, a generative model was used to create 1094 candidate inhibitor molecules. Analysis indicates that the model-generated molecules can comprehend the principles of molecular docking. Moreover, some of these molecules can modify the original molecular frameworks. A molecular screening strategy was devised, leading to the identification of five promising eEF2K inhibitor lead compounds. These five compound molecules demonstrated excellent thermodynamic performance when docked with eEF2K, with Vina scores of −12.12, −16.67, −15.07, −15.99, and −10.55 kcal/mol, respectively, showing a 24.27% improvement over known active inhibitor molecules. Additionally, they exhibited favorable drug-likeness. This study used deep generative models to develop eEF2K inhibitors, enabling the treatment of cancer and neurological disorders.

Photocatalysis is becoming increasingly important in modern chemistry for efficient multicomponent one-pot synthesis. However, predicting the results of photocatalytic reactions using artificial intelligence remains challenging, mostly due to the insufficient number of photocatalytic reactions and the incomplete information on reaction conditions in existing reaction databases. In this study, we curated the Photocatalysis Database (PhotoCatDB), which consists of 6,523 photocatalytic reactions (of which 6,175 are multicomponent) containing reaction condition information such as photocatalysts, bases or acids, additives, and solvents. Before adding reaction conditions to the training data, the attention-based deep learning model PhotoCat pre-trained on USPTO and fine-tuned on PhotoCatDB had a Top-1 accuracy of 78.16%, which was 77.70% higher than the same model trained only on the USPTO database and 14.53% higher than the model fine-tuned by the photocatalytic reactions from Reaxys. After adding reaction conditions to the training data, the Top-1 accuracy of PhotoCat was further increased to 82.25%. In addition, the interpretability of the model was reflected in its attention weights, which can infer the model’s understanding of photocatalytic chemistry. Furthermore, five previously unreported photocatalytic reactions predicted by PhotoCat were successfully validated by wet-lab experiments, demonstrating the potential of the model in identifying and verifying novel photocatalysis reactions of real-world significance.

With the rapid development of radical initiation technologies such as photocatalysis and electrocatalysis, radical reactions have become an increasingly attractive approach for constructing target molecules. However, designing efficient synthetic routes using radical reactions remains a significant challenge due to the inherent complexity and instability of radical intermediates. While computer-aided synthesis planning (CASP) has advanced retrosynthetic analysis for polar reactions, radical reactions have been largely overlooked in AI-driven approaches. In this study, we introduce RadicalRetro, the first deep learning-based retrosynthesis model specifically tailored for radical reactions. Our work is distinguished by three key contributions: (1) RadicalDB: A novel, manually curated database of 21.6 K radical reactions, focusing on high-impact literature and mechanistic clarity, addressing the critical gap in dedicated radical reaction datasets. (2) Model Innovation: By pretraining Chemformer on ZINC-15 and USPTO datasets followed by fine-tuning with RadicalDB, RadicalRetro achieves a Top-1 accuracy of 69.3% in radical retrosynthesis, surpassing the state-of-the-art models LocalRetro and Mol-Transformer by 23.0% and 25.4%, respectively. (3) Interpretability and Practical Utility: Attention weight analysis and case studies demonstrate that RadicalRetro effectively captures radical reaction patterns (e.g., cascade cyclizations and photocatalytic steps) and proposes synthetically viable routes, such as streamlined pathways for Tamoxifen precursors and glycoside derivatives. RadicalRetro’s performance highlights its potential to transform radical-based synthetic planning, offering chemists a robust tool to leverage the unique advantages of radical chemistry in drug synthesis.

With the rapid development of radical initiation technologies such as photocatalysis and electrocatalysis, radical reactions have become an increasingly attractive approach for constructing target molecules. However, determining how to efficiently synthesize a specific target molecule using radical reactions remains a challenging task, requiring years of education, training, and extensive trial-and-error by synthetic chemists. The advancement of computer-aided synthesis planning (CASP) has opened new possibilities for accelerating the optimization of radical reaction pathways through artificial intelligence. In this study, we collected 21.6K entries of radical reaction data to build the Radical Reaction Database (RadicalDB). After pre-training with ZINC-15 and USPTO database, we fine-tuned the Chemformer model using RadicalDB, resulting in the development of RadicalRetro, a retrosynthetic prediction model specialized in radical reactions. RadicalRetro achieved a top-1 prediction accuracy of 69.3% for radical retrosynthesis, outperforming similar models such as LocalRetro and Mol-Transformer by 23.0% and 25.4%, respectively. Case studies and attention weight analyses confirm that RadicalRetro effectively captures the unique characteristics of radical reactions.