Molecular Design with Generative Long Short-term Memory.

Drug discovery benefits from computational models aiding the identification of new chemical matter with bespoke properties. The field of drug design has been particularly revitalized by adaptation of generative machine learning models from the field of natural language processing. These deep neural network models are trained on recognizing molecular structures and generate new molecular entities without relying on pre-determined sets of molecular building blocks and chemical transformations for virtual molecule construction. Implicit representation of chemical knowledge provides an alternative to formulating the molecular design task in terms of the established, explicit chemical vocabulary. Here, we review molecular design approaches from the field of 'artificial intelligence', focusing on instances of deep generative models, and highlight the prospective application of long short-term memory models to hit and lead finding in medicinal chemistry.