تقرير
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks
| العنوان: | Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks |
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| المؤلفون: | Nguyen, Duy M. H., Lukashina, Nina, Nguyen, Tai, Le, An T., Nguyen, TrungTin, Ho, Nhat, Peters, Jan, Sonntag, Daniel, Zaverkin, Viktor, Niepert, Mathias |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning |
| الوصف: | A molecule's 2D representation consists of its atoms, their attributes, and the molecule's covalent bonds. A 3D (geometric) representation of a molecule is called a conformer and consists of its atom types and Cartesian coordinates. Every conformer has a potential energy, and the lower this energy, the more likely it occurs in nature. Most existing machine learning methods for molecular property prediction consider either 2D molecular graphs or 3D conformer structure representations in isolation. Inspired by recent work on using ensembles of conformers in conjunction with 2D graph representations, we propose $\mathrm{E}$(3)-invariant molecular conformer aggregation networks. The method integrates a molecule's 2D representation with that of multiple of its conformers. Contrary to prior work, we propose a novel 2D-3D aggregation mechanism based on a differentiable solver for the \emph{Fused Gromov-Wasserstein Barycenter} problem and the use of an efficient conformer generation method based on distance geometry. We show that the proposed aggregation mechanism is $\mathrm{E}$(3) invariant and propose an efficient GPU implementation. Moreover, we demonstrate that the aggregation mechanism helps to significantly outperform state-of-the-art molecule property prediction methods on established datasets. Comment: Accepted at ICML 2024 |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2402.01975Test |
| رقم الانضمام: | edsarx.2402.01975 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |