  Design of new drugs is a challenging process: a candidate molecule should
satisfy multiple conditions to act properly and make the least side-effect --
perfect candidates selectively attach to and influence only targets, leaving
off-targets intact. The amount of experimental data about various properties of
molecules constantly grows, promoting data-driven approaches. However, the
applicability of typical predictive machine learning techniques can be
substantially limited by a lack of experimental data about a particular target.
For example, there are many known Thrombin inhibitors (acting as
anticoagulants), but a very limited number of known Protein C inhibitors
(coagulants). In this study, we present our approach to suggest new inhibitor
candidates by building an effective representation of chemical space. For this
aim, we developed a deep learning model -- autoencoder, trained on a large set
of molecules in the SMILES format to map the chemical space. Further, we
applied different sampling strategies to generate novel coagulant candidates.
Symmetrically, we tested our approach on anticoagulant candidates, where we
were able to predict their inhibition towards Thrombin. We also compare our
approach with MegaMolBART -- another deep learning generative model, but
exploiting similar principles of navigation in a chemical space.
