Predicting Molecular Ground-State Conformation via Conformation Optimization

要約

Predicting molecular ground-state conformation (i.e., energy-minimized conformation) is crucial for many chemical applications such as molecular docking and property prediction.
Classic energy-based simulation is time-consuming when solving this problem while existing learning-based methods have advantages in computational efficiency but sacrifice accuracy and interpretability.
In this work, we propose a novel and effective method to bridge the energy-based simulation and the learning-based strategy, which designs and learns a Wasserstein gradient flow-driven SE(3)-Transformer, called WGFormer, for molecular ground-state
立体構造予測。
Specifically, our method tackles this task within an auto-encoding framework, which encodes low-quality conformations by the proposed WGFormer and decodes corresponding ground-state conformations by an MLP.
Wgformerのアーキテクチャは、Wasserstein勾配の流れに対応しています。原子の潜在混合モデルで定義されたエネルギー関数を最小化することにより、分子コンフォメーションを最適化し、それによりパフォーマンスと解釈性を大幅に改善します。
広範な実験は、私たちの方法が一貫して最先端の競合他社よりも優れていることを示しており、分子の基底状態の立体構造を予測するための新しい洞察力のあるパラダイムを提供します。

要約(オリジナル)

Predicting molecular ground-state conformation (i.e., energy-minimized conformation) is crucial for many chemical applications such as molecular docking and property prediction. Classic energy-based simulation is time-consuming when solving this problem while existing learning-based methods have advantages in computational efficiency but sacrifice accuracy and interpretability. In this work, we propose a novel and effective method to bridge the energy-based simulation and the learning-based strategy, which designs and learns a Wasserstein gradient flow-driven SE(3)-Transformer, called WGFormer, for molecular ground-state conformation prediction. Specifically, our method tackles this task within an auto-encoding framework, which encodes low-quality conformations by the proposed WGFormer and decodes corresponding ground-state conformations by an MLP. The architecture of WGFormer corresponds to Wasserstein gradient flows — it optimizes molecular conformations by minimizing an energy function defined on the latent mixture models of atoms, thereby significantly improving performance and interpretability. Extensive experiments show that our method consistently outperforms state-of-the-art competitors, providing a new and insightful paradigm to predict molecular ground-state conformation.

arxiv情報

提供元, 利用サービス

arxiv.jp, Google