Determining molecular structures from spectroscopic data remains fundamentally challenging because the inverse problem is intrinsically underdetermined: individual spectra are sparse, low-dimensional, and encode only partial structural evidence relative to the vast space of possible molecules. We address this challenge by formulating automated structure elucidation as a scalable hypothesis-refinement paradigm that tightly integrates spectral evidence with large-scale molecular priors. To supply structure-resolving NMR signals for multimodal learning, we construct \textbf{QM9SPIN}, a DFT-derived dataset comprising diverse 1D and 2D spectra, including J-coupling, DEPT experiments, and explicit spin--spin interactions. On this foundation, we introduce \textbf{SpectroMol}, a spectrum-to-structure model that proposes chemically valid molecular hypotheses conditioned on multimodal spectral inputs. Complementarily, we develop \textbf{MS-Mol2Mol}, a high-resolution mass-constrained molecular generator that integrates molecular formula, exact mass, and degree of unsaturation within a conditional generative prior trained on 400 million molecules, ensuring global compositional consistency and chemically realistic refinement. The integrated system achieves 93.8\% top-1 accuracy on the simulated benchmark, adapts effectively from simulated to experimental spectra with limited experimental fine-tuning, and further improves experimental predictions through mass-guided refinement, establishing a scalable route toward automated, data-driven organic structure elucidation.
Chengchun Liu, Zhiyuan Yan, Li Yuan et al.· 0 citations
Accurate identification of protein binding sites is essential for understanding biological mechanisms and advancing drug design. However, many structure-based predictors rely on spatial graphs whose topology remains fixed throughout message passing, making them sensitive to structural noise and difficult to transfer across ligand modalities. To address this issue, we propose DiConSite, a topology-adaptive and reusable architecture for residue-level binding site prediction across ligand-specific tasks. DiConSite is centered on a Latent Topological Evolution (LTE) module that augments the initial Euclidean graph with a latent functional topology. A Hierarchical Topological Distillation (HTD) objective and a Dynamic Curriculum Distillation (DCD) schedule are further introduced as LTE-dependent optimization stabilizers: they align relational structure across network depths only after the underlying topology has been refined. Extensive experiments across nine benchmarks show that DiConSite achieves consistently strong and often best-performing results, while improving robustness to structural uncertainty and cross-modal variation. By combining protein language model embeddings with topology-adaptive geometric reasoning, DiConSite offers a reusable framework for residue-level protein interaction analysis.
Shouzhi Chen, Zhenchao Tang, Linlin You et al.· IEEE Transactions on Pattern...· 1 citation
HME is presented, a framework that combines multiple views of molecules to improve molecular understanding and design and enables bidirectional navigation of the chemical-linguistic space, achieving consistent improvements across molecular comprehension and design tasks over strong baselines.
Liuzhenghao Lv, Hao Li, Yu Wang et al.· Nature Communications· 0 citations