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Future Med Chem. 2019 Jan 30. doi: 10.4155/fmc-2018-0358. [Epub ahead of print]

Deep learning for molecular generation.

Author information

1
Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, PR China.
2
BNLMS, State Key Laboratory for Structural Chemistry of Unstable & Stable Species, College of Chemistry & Molecular Engineering, Peking University, Beijing, 100871, PR China.
3
PTN Graduate Program, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, PR China.

Abstract

De novo drug design aims to generate novel chemical compounds with desirable chemical and pharmacological properties from scratch using computer-based methods. Recently, deep generative neural networks have become a very active research frontier in de novo drug discovery, both in theoretical and in experimental evidence, shedding light on a promising new direction of automatic molecular generation and optimization. In this review, we discussed recent development of deep learning models for molecular generation and summarized them as four different generative architectures with four different optimization strategies. We also discussed future directions of deep generative models for de novo drug design.

KEYWORDS:

drug design; automatic molecular generation; deep generative neural networks; molecular optimization

PMID:
30698019
DOI:
10.4155/fmc-2018-0358

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