  The potential number of drug like small molecules is estimated to be between
10^23 and 10^60 while current databases of known compounds are orders of
magnitude smaller with approximately 10^8 compounds. This discrepancy has led
to an interest in generating virtual libraries using hand crafted chemical
rules and fragment based methods to cover a larger area of chemical space and
generate chemical libraries for use in in silico drug discovery endeavors. Here
it is explored to what extent a recurrent neural network with long short term
memory cells can figure out sensible chemical rules and generate synthesizable
molecules by being trained on existing compounds encoded as SMILES. The
networks can to a high extent generate novel, but chemically sensible
molecules. The properties of the molecules are tuned by training on two
different datasets consisting of fragment like molecules and drug like
molecules. The produced molecules and the training databases have very similar
distributions of molar weight, predicted logP, number of hydrogen bond
acceptors and donors, number of rotatable bonds and topological polar surface
area when compared to their respective training sets. The compounds are for the
most cases synthesizable as assessed with SA score and Wiley ChemPlanner.
