Conformation, which refers to the different atomic arrangements a molecule can adopt in three-dimensional space, is one of the core factors determining a molecule's physicochemical properties. However, in current mainstream molecular machine learning modeling practices, conformational information is often ignored or simplified. Most prediction tasks still rely on two-dimensional structure representations, and there is even inconsistency in using the most stable conformational form. The lack of conformational information has become a significant factor limiting model prediction accuracy and also exposes the shortcomings of existing molecular representation methods in handling conformation-sensitive tasks.
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