  De novo design of molecules has recently enjoyed the power of generative deep
neural networks. Current approaches aim to generate molecules either resembling
the properties of the molecules of the training set or molecules that are
optimized with respect to specific physicochemical properties. None of the
methods generates molecules specific to a target protein. In the approach
presented here, we introduce a method which is conditioned on the protein
target sequence to generate de novo molecules that are relevant to the target.
We use an implementation adapted from Google's "Show and Tell" image caption
generation method, to generate SMILES strings of molecules from protein
sequence embeddings generated by a deep bi-directional language model ELMo.
ELMo is used to generate contextualized embedding vectors of the protein
sequence. Using reinforcement learning, the trained model is further optimized
through augmented episodic likelihood to increase the diversity of the
generated compounds compared to the training set. We used the model to generate
compounds for two major drug target families, i.e. for GPCRs and Tyrosine
Kinase targets. The model generated compounds which are structurally different
form the training set, while also being more similar to compounds known to bind
to the two families of drug targets compared to a random set of molecules. The
compounds further display reasonable synthesizability and drug-likeness scores.
