Application of Generative Autoencoder in De Novo Molecular Design

A major challenge in computational chemistry is the generation of novel molecular structures with desirable pharmacological and physiochemical properties. In this work, we investigate the potential use of autoencoder, a deep learning methodology, for de novo molecular design. Various generative auto...

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Veröffentlicht in:Molecular informatics Jg. 37; H. 1-2
Hauptverfasser: Blaschke, Thomas, Olivecrona, Marcus, Engkvist, Ola, Bajorath, Jürgen, Chen, Hongming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Germany Wiley Subscription Services, Inc 01.01.2018
John Wiley and Sons Inc
Schlagworte:
Autoencoder
Chemical fingerprinting
chemoinformatics
Computational chemistry
Computer applications
de novo molecular design
Deep Learning
Dopamine
Drug Design
Energy management
inverse QSAR
Machine learning
Pharmacology
Physiochemistry
Quantitative Structure-Activity Relationship
chemoinformatics
deep learning
inverse QSAR
Autoencoder
de novo molecular design
ISSN:1868-1743, 1868-1751, 1868-1751
Online-Zugang:Volltext
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Zusammenfassung:A major challenge in computational chemistry is the generation of novel molecular structures with desirable pharmacological and physiochemical properties. In this work, we investigate the potential use of autoencoder, a deep learning methodology, for de novo molecular design. Various generative autoencoders were used to map molecule structures into a continuous latent space and vice versa and their performance as structure generator was assessed. Our results show that the latent space preserves chemical similarity principle and thus can be used for the generation of analogue structures. Furthermore, the latent space created by autoencoders were searched systematically to generate novel compounds with predicted activity against dopamine receptor type 2 and compounds similar to known active compounds not included in the trainings set were identified.
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ISSN:1868-1743
1868-1751
1868-1751
DOI:10.1002/minf.201700123