  Designing a network to learn a molecule structure given its physical/chemical
properties is a hard problem, but is useful for drug discovery tasks. In this
paper, we incorporate higher-order relational learning of Factor Graphs with
strong approximation power of Neural Networks to create a molecule-structure
learning network that has strong generalization power and can enforce
higher-order relationship and valence constraints. We further propose methods
to tackle problems such as the efficient design of factor nodes, conditional
parameter sharing among factors, and symmetry problems in molecule structure
prediction. Our experiment evaluation shows that the factor learning is
effective and outperforms related methods.
