著者

Francesca Grisoni Gisbert Schneider

出版者

Division of Chemical Information and Computer Sciences The Chemical Society of Japan

雑誌

Journal of Computer Aided Chemistry (ISSN:13458647)

巻号頁・発行日

vol.20, pp.35-42, 2019 (Released:2019-12-26)

参考文献数

65

被引用文献数

1

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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.