论文标题
基于注意分子合奏的学习
Attention-Based Learning on Molecular Ensembles
论文作者
论文摘要
小分子配体的三维形状和构象对于生物分子识别至关重要，但是编码3D几何形状并未改善基于配体的虚拟筛选方法。我们描述了一种直接在小分子构象合奏上运行的端到端深度学习方法，并确定了小分子的关键构象姿势。我们的网络利用两个级别的表示学习：1）首先使用图形神经网络对单个构象异构体首先编码为空间图，而2）使用注意机制在各个实例上汇总进行采样的构象合奏表示为集合。我们证明了这种方法在基于双齿配体的双齿配位的简单任务上的可行性，并展示了基于注意力的合并如何根据分子几何形状阐明任务中的关键构象姿势。这项工作说明了如何为基于小分子的虚拟筛选而进一步开发基于集合的学习方法。
The three-dimensional shape and conformation of small-molecule ligands are critical for biomolecular recognition, yet encoding 3D geometry has not improved ligand-based virtual screening approaches. We describe an end-to-end deep learning approach that operates directly on small-molecule conformational ensembles and identifies key conformational poses of small-molecules. Our networks leverage two levels of representation learning: 1) individual conformers are first encoded as spatial graphs using a graph neural network, and 2) sampled conformational ensembles are represented as sets using an attention mechanism to aggregate over individual instances. We demonstrate the feasibility of this approach on a simple task based on bidentate coordination of biaryl ligands, and show how attention-based pooling can elucidate key conformational poses in tasks based on molecular geometry. This work illustrates how set-based learning approaches may be further developed for small molecule-based virtual screening.
