Related papers: Equivalent Distance Geometry Error for Molecular Conformation Comparison

Equivalent Distance Geometry Error for Molecular Conformation Comparison

URL: http://arxiv.org/abs/2201.08714v2
Date: Tue, 15 Mar 2022 04:39:32 GMT
Title: Equivalent Distance Geometry Error for Molecular Conformation Comparison
Authors: Shuwen Yang, Tianyu Wen, Ziyao Li and Guojie Song
Abstract summary: We propose Equivalent Distance Geometry Error (EDGE) to calculate the differential discrepancy between conformations. In the improved version of our method, the optimization features minimizing linear transformations of atom-pair distances within 3-hop.
Score: 26.331322944298208
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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