Update and rename DeePMD_24_04_2025.md to Uni-Mol_24_04_2025.md

source/_posts/DeePMD_24_04_2025.md → source/_posts/Uni-Mol_24_04_2025.md

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-title: "What Can DP Do too? | Revealing the Critical Role of Molecular Conformations in AI Prediction Performance"
+title: "What Can Uni-Mol Do too? | Revealing the Critical Role of Molecular Conformations in AI Prediction Performance"
 date: 2025-04-24
 categories: 
 - DeePMD
@@ -11,7 +11,6 @@ Conformation, which refers to the different atomic arrangements a molecule can a
 
 Recently, Yu Hamakawa et al. published a study titled "Understanding Conformation Importance in Data-Driven Property Prediction Models" in the Journal of Chemical Information and Modeling. This research systematically evaluates the value of conformational information in molecular modeling and empirically compares the performance of mainstream models, including Uni-Mol, across multiple datasets. The study shows that by fully modeling conformational information, AI can significantly improve performance in multiple molecular property prediction tasks, with Uni-Mol demonstrating exceptional advantages in particular.
 
-Paper Link : https://j1q.cn/qspF6Zl5
 
 ## Dataset Introduction: Four Types of Task Scenarios with Conformational Diversity