The blood-brain barrier (BBB) presents a challenge for central nervous system (CNS) therapies. Receptor-mediated transcytosis offers a solution, but existing receptor targets are ubiquitous across CNS and peripheral tissues, causing unintended exposure. We identified carbonic anhydrase IV (CA-IV) as a brain-enriched receptor enabling engineered viral capsids to cross the BBB. However, it is unclear whether CA-IV can also mediate non-viral delivery. We thus designed a reactive small-molecule shuttle, derived from an FDA-approved binder, that couples to proteins and oligonucleotides in a single step. We validated the binding of conjugated molecules to multiple mammalian CA-IV orthologs and subsequent internalization in cell-based assays. After systemic dosing, CA-IV-targeted antibody conjugates crossed the BBB in mice and neonatal macaques, preferentially accumulating in the brain and sustaining parenchymal levels for at least 7 days. This BrainCAB (Brain access through Carbonic Anhydrase-binder Bioconjugation) technology offers a compact, modular shuttle for selective and prolonged CNS delivery of large molecules.
Xiaozhe Ding, Xinhong Chen, Philip Boehm et al.· Nature Chemical Biology· 0 citations
Reaction virtual screening and discovery are fundamental challenges in chemistry and material science, where traditional graph neural networks (GNNs) struggle to model multi-reactant interactions. In this work, we propose ChemHGNN, a hypergraph neural network (HGNN) framework that effectively captures high-order relationships in reaction networks. Unlike GNNs, which require constructing complete graphs for multi-reactant reactions, ChemHGNN naturally models multi-reactant reactions through hyperedges, enabling more expressive reaction representations. To address key challenges—such as combinatorial explosion, model collapse, and chemically invalid negative samples—we introduce a reaction center-aware negative sampling strategy (RCNS) and a hierarchical embedding approach combining molecule, reaction and hypergraph level features. Experiments on the USPTO dataset demonstrate that ChemHGNN significantly outperforms HGNN and GNN baselines, particularly in large-scale settings, while maintaining interpretability and chemical plausibility. Our work establishes ChemHGNN as a superior alternative for reaction virtual screening and discovery, offering a chemically informed framework for accelerating reaction discovery. Click here for GitHub repository.
Xiaobao Huang, Yihong Ma, Anjali Gurajapu et al.· Proceedings of the 32nd ACM...· 0 citations
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