De novo Molecular Design with Generative Long Short-term Memory

Francesca Grisoni, Gisbert Schneider

Research output: Contribution to journalReview article

12 Citations (Scopus)


Drug discovery benefits from computational models aiding the identification of new chemical matter with bespoke properties. The field of de novo 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 de novo 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.

Original languageEnglish
Pages (from-to)1006-1011
Number of pages6
Issue number12
Publication statusPublished - 18 Dec 2019
Externally publishedYes


  • Drug Design
  • Machine Learning
  • Memory, Short-Term
  • Neural Networks, Computer
  • Prospective Studies
  • Deep learning
  • LSTM
  • Drug discovery
  • Neural network
  • Chemoinformatics


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