De novo Molecular Design with Generative Long Short-term Memory

Authors

  • Francesca Grisoni ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland
  • Gisbert Schneider ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland;, Email: gisbert.schneider@pharma.ethz.ch

DOI:

https://doi.org/10.2533/chimia.2019.1006

PMID:

31883552

Keywords:

Chemoinformatics, Deep learning, Drug discovery, Lstm, Neural network

Abstract

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.

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Published

2019-12-18

How to Cite

[1]
F. Grisoni, G. Schneider, Chimia 2019, 73, 1006, DOI: 10.2533/chimia.2019.1006.