  Machine learning has the potential to automate molecular design and
drastically accelerate the discovery of new functional compounds. Towards this
goal, generative models and reinforcement learning (RL) using string and graph
representations have been successfully used to search for novel molecules.
However, these approaches are limited since their representations ignore the
three-dimensional (3D) structure of molecules. In fact, geometry plays an
important role in many applications in inverse molecular design, especially in
drug discovery. Thus, it is important to build models that can generate
molecular structures in 3D space based on property-oriented geometric
constraints. To address this, one approach is to generate molecules as 3D point
clouds by sequentially placing atoms at locations in space -- this allows the
process to be guided by physical quantities such as energy or other properties.
However, this approach is inefficient as placing individual atoms makes the
exploration unnecessarily deep, limiting the complexity of molecules that can
be generated. Moreover, when optimizing a molecule, organic and medicinal
chemists use known fragments and functional groups, not single atoms. We
introduce a novel RL framework for scalable 3D design that uses a hierarchical
agent to build molecules by placing molecular substructures sequentially in 3D
space, thus attempting to build on the existing human knowledge in the field of
molecular design. In a variety of experiments with different substructures, we
show that our agent, guided only by energy considerations, can efficiently
learn to produce molecules with over 100 atoms from many distributions
including drug-like molecules, organic LED molecules, and biomolecules.
