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
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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