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Automating molecular design using deep reinforcement learning (RL) has thepotential to greatly accelerate the search for novel materials. Despite recentprogress on leveraging graph representations to design molecules, such methodsare fundamentally limited by the lack of three-dimensional (3D) information. Inlight of this, we propose a novel actor-critic architecture for 3D moleculardesign that can generate molecular structures unattainable with previousapproaches. This is achieved by exploiting the symmetries of the design processthrough a rotationally covariant state-action representation based on aspherical harmonics series expansion. We demonstrate the benefits of ourapproach on several 3D molecular design tasks, where we find that building insuch symmetries significantly improves generalization and the quality ofgenerated molecules.

Symmetry-Aware Actor-Critic for 3D Molecular Design