Update Uni-Mol_10_04_2025.md (#240)

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 Enzymes are the core catalysts of life activities, and the prediction of their functions has important applications in various fields. However, traditional methods of enzyme classification, such as EC numbers or protein family classification, rely on manual classification, which can lead to inaccurate classification granularity. Moreover, these methods lack the ability to characterize the dynamic structural transformation of substrates and products during the reaction process, making it difficult to accurately understand the actual functions and catalytic mechanisms of enzymes. Against this backdrop, ReactZyme, developed by Hua et al., brings new hope to the study of enzyme functions. Uni - Mol, as the core engine for molecular modeling, provides powerful support for capturing the complex three - dimensional interactions between enzymes and substrates. This achievement was presented at the Datasets and Benchmarks track of the 38th Conference on Neural Information Processing Systems (NeurIPS 2024), and its code and data have been open - sourced(https://github.com/WillHua127/ReactZyme).
 
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 ## Research Background
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 The existing enzyme function annotation systems, EC (Enzyme Commission) and GO (Gene Ontology), are widely used. However, the manual - classification - based approach has two major drawbacks.
 
 Firstly, inaccurate classification granularity affects the accurate determination of enzyme functions. EC mainly classifies enzymes according to the types of chemical reactions they catalyze and substrates. This may group enzymes with significantly different functions into the same category or over - segment enzymes with similar functions. For example, some enzymes with marked differences in catalytic mechanisms and reaction conditions are grouped together just because their substrates are similar. Although GO annotation is more comprehensive, it often lacks precise descriptions in specific function definitions, failing to meet the requirements of in-depth research.