  Straight-forward conformation generation models, which generate 3-D
structures directly from input molecular graphs, play an important role in
various molecular tasks with machine learning, such as 3D-QSAR and virtual
screening in drug design. However, existing loss functions in these models
either cost overmuch time or fail to guarantee the equivalence during
optimization, which means treating different items unfairly, resulting in poor
local geometry in generated conformation. So, we propose Equivalent Distance
Geometry Error (EDGE) to calculate the differential discrepancy between
conformations where the essential factors of three kinds in conformation
geometry (i.e. bond lengths, bond angles and dihedral angles) are equivalently
optimized with certain weights. And in the improved version of our method, the
optimization features minimizing linear transformations of atom-pair distances
within 3-hop. Extensive experiments show that, compared with existing loss
functions, EDGE performs effectively and efficiently in two tasks under the
same backbones.
