In this work, we use transformer models for this end. Transformer models were first proposed in 2017 for translation between natural languages (Fig. ), and have gained a lot of popularity due to successful applications to various tasks, including machine translation , predictions of chemical reaction outcomes, and protein structure prediction. In this paradigm, we consider hit expansion as a process of "translation" of known molecules active against a certain target into novel molecules that should be active (preferably, more active) against the same target (Fig. ). For this translation, we write input and output molecules in the form of SELFIES, a representation thatunlike a more popular SMILES representationautomatically ensures chemical validity of nearly 100% of random strings, thereby helping an ML model to focus on learning chemically informative trends and rules in the training set, without a need to learn grammar rules of composing chemically valid structures first.