  We report a method to convert discrete representations of molecules to and
from a multidimensional continuous representation. This model allows us to
generate new molecules for efficient exploration and optimization through
open-ended spaces of chemical compounds. A deep neural network was trained on
hundreds of thousands of existing chemical structures to construct three
coupled functions: an encoder, a decoder and a predictor. The encoder converts
the discrete representation of a molecule into a real-valued continuous vector,
and the decoder converts these continuous vectors back to discrete molecular
representations. The predictor estimates chemical properties from the latent
continuous vector representation of the molecule. Continuous representations
allow us to automatically generate novel chemical structures by performing
simple operations in the latent space, such as decoding random vectors,
perturbing known chemical structures, or interpolating between molecules.
Continuous representations also allow the use of powerful gradient-based
optimization to efficiently guide the search for optimized functional
compounds. We demonstrate our method in the domain of drug-like molecules and
also in the set of molecules with fewer that nine heavy atoms.
