[Special Issue for Honor Award dedicating to Prof Kimito Funatsu]Molecular Design With Long Short-Term Memory Networks

• Published in: Journal of Computer Aided Chemistry Vol. 20; pp. 35 - 42
• Main Authors: Grisoni, Francesca, Schneider, Gisbert
• Format: Journal Article
• Language: English
• Published: Division of Chemical Information and Computer Sciences The Chemical Society of Japan 2019
• Subjects: de novo design; long short-term memory network (LSTM); recurrent neural network (RNN); transfer learning
• ISSN: 1345-8647; 1345-8647
• DOI: 10.2751/jcac.20.35

Computer-assisted de novo drug design has been a central research topic in the field of chemoinformatics for approximately 30 years. Professor Kimito Funatsu’s research has been a formative component in these developments. His seminal work has contributed inverse quantitative-structure-activity relationship (QSAR) models for small molecule and peptide design. This article highlights a class of recurrent neural networks, so-called long short-term memory (LSTM) networks for generative molecular design, which further the conceptual approach of inverse QSAR. We review the LSTM method for molecular design along with selected practical applications.