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Comparison Between SMILES-Based Differential Neural Computer and Recurrent Neural Network Architectures for De Novo Molecule Design

preprint
submitted on 02.09.2019 and posted on 04.09.2019 by Simon Viet Johansson, Oleksii Prykhodko, Josep Arús-Pous, Ola Engkvist, Hongming Chen
In recent years, deep learning for de novo molecular generation has become a rapidly growing research area. Recurrent neural networks (RNN) using the SMILES molecular representation is one of the most common approaches used. Recent study shows that the differentiable neural computer (DNC) can make considerable improvement over the RNN for modeling of sequential data. In the current study, DNC has been implemented as an extension to REINVENT, an RNN-based model that has already been used successfully to make de novo molecular design. The model was benchmarked on its capacity to learn the SMILES language on the GDB-13 and MOSES datasets. The DNC shows improvement on all test cases conducted at the cost of significantly increased computational time and memory consumption.

Funding

European Union’s Horizon 2020 research and innovation program; Marie Skłodowska-Curie grant agreement no. 676434, “Big Data in Chemistry” (“BIGCHEM,” http://bigchem.eu)

History

Email Address of Submitting Author

Simon.johansson@astrazeneca.com

Institution

AstraZeneca AB

Country

Sweden

ORCID For Submitting Author

0000-0001-9139-6378

Declaration of Conflict of Interest

Authors declare no conflict of interest

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