Automating molecular design using deep reinforcement learning (RL) has the potential to greatly accelerate the search for novel materials. Despite recent progress on leveraging graph representations to design molecules, such methods are fundamentally limited by the lack of three-dimensional (3D) information. In light of this, we propose a novel actor-critic architecture for 3D molecular design that can generate molecular structures unattainable with previous approaches. This is achieved by exploiting the symmetries of the design process through a rotationally covariant state-action representation based on a spherical harmonics series expansion. We demonstrate the benefits of our approach on several 3D molecular design tasks, where we find that building in such symmetries significantly improves generalization and the quality of generated molecules.

使用深度强化学习自动化分子设计，在利用基于图形表示法设计分子的进步存在基本限制的情况下，我们提出了一种新的三维分子设计策略，该策略通过基于球谐级数展开的旋转协变的状态-动作表示法利用了设计过程的对称性，进而产生了先前方法无法实现的分子结构，并通过几个3D分子设计任务展示了这种策略的优越性。

面向对称性的三维分子设计的演员-评论家算法