期刊
MOLECULAR INFORMATICS
卷 37, 期 1-2, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/minf.201700123
关键词
Autoencoder; chemoinformatics; de novo molecular design; deep learning; inverse QSAR
类别
资金
- European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant [676434]
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 type2 and compounds similar to known active compounds not included in the trainings set were identified.
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