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Van Deursen, R.* ; Ertl, P.* ; Tetko, I.V. ; Godin, G.*

GEN: Highly efficient SMILES explorer using autodidactic generative examination networks.

J. Cheminformatics 12:22 (2020)
Verlagsversion DOI
Open Access Gold
Creative Commons Lizenzvertrag
Recurrent neural networks have been widely used to generate millions of de novo molecules in defined chemical spaces. Reported deep generative models are exclusively based on LSTM and/or GRU units and frequently trained using canonical SMILES. In this study, we introduce Generative Examination Networks (GEN) as a new approach to train deep generative networks for SMILES generation. In our GENs, we have used an architecture based on multiple concatenated bidirectional RNN units to enhance the validity of generated SMILES. GENs autonomously learn the target space in a few epochs and are stopped early using an independent online examination mechanism, measuring the quality of the generated set. Herein we have used online statistical quality control (SQC) on the percentage of valid molecular SMILES as examination measure to select the earliest available stable model weights. Very high levels of valid SMILES (95-98%) can be generated using multiple parallel encoding layers in combination with SMILES augmentation using unrestricted SMILES randomization. Our trained models combine an excellent novelty rate (85-90%) while generating SMILES with strong conservation of the property space (95-99%). In GENs, both the generative network and the examination mechanism are open to other architectures and quality criteria.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Autonomous Learning ; Gen ; Gan ; Rnn ; Lstm ; Gru ; Bilstm ; Bigru ; Ai ; Smiles ; Generator ; Quality Control ; Sqc; Chemical Space; Design
e-ISSN 1758-2946
Quellenangaben Band: 12, Heft: 1, Seiten: , Artikelnummer: 22 Supplement: ,
Verlag BioMed Central
Verlagsort Campus, 4 Crinan St, London N1 9xw, England
Begutachtungsstatus Peer reviewed