While the determination of enzyme kinetic parameters traditionally relies on prolonged and costly experimental methods, machine learning models have recently demonstrated promise as predictive tools. These models still have limitations, such as limited training data and insufficient sensitivity to mutations at import...
Jia-He Qiu, Zong-Ying Lin, Ke-Wei Chen et al.· Journal of Chemical Informat...· 0 citations
Protein mutation effect generation asks a model to describe the functional consequence of a point mutation in natural language. Existing protein-to-text systems typically encode mutation information into undifferentiated representations, overlooking the organization of mutation-induced evidence across structural and bi...
Liuzhenghao Lv, Yu-Yang Liu, Yu-Yang Gao et al.· 0 citations
TCRspec, an interpretable multimodal framework combining sequence embeddings, gene-usage features, and complex-level structural representations, providing a structure-informed framework for TCR specificity prediction, is developed.
Jia Zou, Zong-Ying Lin, Yi-Min Wang et al.· Journal of Chemical Informat...· 0 citations
UniWorld-View is introduced, a unified framework for controllable large-baseline novel view synthesis from monocular inputs that integrates explicit 3D guidance with generative diffusion modeling to enable precise camera control and geometrically consistent view generation.
Hai-Yang Zhou, Wang-Bo Yu, Chaoran Feng et al.· 2 citations
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...
Chengchun Liu, Zhiyuan Yan, Li Yuan et al.· 0 citations
By combining protein language model embeddings with topology-adaptive geometric reasoning, DiConSite offers a reusable framework for residue-level protein interaction analysis and achieves consistently strong and often best-performing results, while improving robustness to structural uncertainty and cross-modal variati...
Shou-Zhi Chen, Zhen-Chao Tang, Lin-Lin 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
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