Richang Hong

Abstract A comprehensive understanding of molecular structures is important for the prediction of molecular ground-state conformation involving property information. Recently, state space models (e.g., Mamba) have emerged as a powerful paradigm for sequence modeling, and have achieved remarkable success in language and vision tasks. However, towards molecular ground-state conformation prediction, exploiting Mamba to understand molecular structure is underexplored.