Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

In de novo drug design, computational strategies are used to
generate novel molecules with good affinity to the desired biological
target. In this work, we show that recurrent neural networks can be
trained as generative models for molecular structures, similar to
statistical language models in natural language processing. We demonstrate
that the properties of the generated molecules correlate very well
with the properties of the molecules used to train the model. In order
to enrich libraries with molecules active toward a given biological
target, we propose to fine-tune the model with small sets of molecules,
which are known to be active against that target. Against Staphylococcus aureus, the model reproduced 14% of 6051
hold-out test molecules that medicinal chemists designed, whereas
against Plasmodium falciparum (Malaria), it reproduced
28% of 1240 test molecules. When coupled with a scoring function,
our model can perform the complete de novo drug design
cycle to generate large sets of novel molecules for drug discovery.