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Direct Molecular Conformation Generation

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arxiv 2202.01356 v2 pith:YFFWOLMM submitted 2022-02-03 cs.AI

classification cs.AI
keywords conformationcoordinatesmethodatomsconformationsmoleculardirectdirectly
verification ladder T0 review T1 audit T2 compute T3 formal
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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 methods usually first predict the interatomic distances, the gradients of interatomic distances or the local structures (e.g., torsion angles) of a molecule, and then reconstruct its 3D conformation. How to directly generate the conformation without the above intermediate values is not fully explored. In this work, we propose a method that directly predicts the coordinates of atoms: (1) the loss function is invariant to roto-translation of coordinates and permutation of symmetric atoms; (2) the newly proposed model adaptively aggregates the bond and atom information and iteratively refines the coordinates of the generated conformation. Our method achieves the best results on GEOM-QM9 and GEOM-Drugs datasets. Further analysis shows that our generated conformations have closer properties (e.g., HOMO-LUMO gap) with the groundtruth conformations. In addition, our method improves molecular docking by providing better initial conformations. All the results demonstrate the effectiveness of our method and the great potential of the direct approach. The code is released at https://github.com/DirectMolecularConfGen/DMCG

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow

    cs.LG 2025-07 conditional novelty 6.0 of 10

    SO(3)-Averaged Flow matching with reflow and distillation enables high-quality one-step molecular conformer generation, reporting new SOTA on GEOM-QM9 and strong one-step results on GEOM-Drugs.

  2. Graph Neural Networks in Modern AI-aided Drug Discovery

    q-bio.BM 2025-06 conditional novelty 1.0 of 10

    A comprehensive model-centric review of graph neural network methods and applications in AI-aided drug discovery, from molecular representation to synthesis planning.

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