Direct Molecular Conformation Generation
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Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology. Previous distance-based methods first predict interatomic distances and then generate conformations based on them, which could result in conflicting distances. In this work, we propose a method that directly predicts the coordinates of atoms. We design a dedicated loss function for conformation generation, which is invariant to roto-translation of coordinates of conformations and permutation of symmetric atoms in molecules. We further design a backbone model that stacks multiple blocks, where each block refines the conformation generated by its preceding block. Our method achieves state-of-the-art results on four public benchmarks: on small-scale GEOM-QM9 and GEOM-Drugs which have 200K training data, we can improve the previous best matching score by 3.5% and 28.9%; on large-scale GEOM-QM9 and GEOM-Drugs which have millions of training data, those two improvements are 47.1% and 36.3%. This shows the effectiveness of our method and the great potential of the direct approach. Our code is released at <https://github.com/DirectMolecularConfGen/DMCG>.
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