“…The physics-based models such as SimRNA [26,27], IsRNA [28][29][30], iFold [31], NAST [32], HiRE-RNA [33], and our model of salt effect [34][35][36][37][38][39][40], are generally based on coarsegrained (CG) representations, specified CG force fields, and certain conformation sampling strategies. The knowledgebased models such as MC-fold/MC-sym pipeline, FARNA [25], Vfold3D [41][42][43][44], RNAComposer [45,46], and 3d RNA [47,48], are generally based on various fragment libraries and fragment-assembly strategies. Generally, an RNA 3D structure prediction model generally generates a large number of 3D structure candidates for a target RNA, and consequently, a reliable statistical potential/scoring function is required to identify a structure closest to the native one [49,50].…”