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Karpov, P. ; Godin, G.* ; Tetko, I.V.

Transformer-CNN: Swiss knife for QSAR modeling and interpretation.

J. Cheminformatics 12:17 (2020)
Publ. Version/Full Text DOI PMC
Open Access Gold
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We present SMILES-embeddings derived from the internal encoder state of a Transformer [1] model trained to canonize SMILES as a Seq2Seq problem. Using a CharNN [2] architecture upon the embeddings results in higher quality interpretable QSAR/QSPR models on diverse benchmark datasets including regression and classification tasks. The proposed Transformer-CNN method uses SMILES augmentation for training and inference, and thus the prognosis is based on an internal consensus. That both the augmentation and transfer learning are based on embeddings allows the method to provide good results for small datasets. We discuss the reasons for such effectiveness and draft future directions for the development of the method. The source code and the embeddings needed to train a QSAR model are available on https://github.com/bigchem/transformer-cnn. The repository also has a standalone program for QSAR prognosis which calculates individual atoms contributions, thus interpreting the model's result. OCHEM [3] environment (https://ochem.eu) hosts the on-line implementation of the method proposed.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Augmentation ; Character-based Models ; Cheminformatics ; Classification ; Convolutional Neural Neural Networks ; Embeddings ; Qsar ; Regression ; Smiles ; Transformer Model; Aqueous Solubility; Neural-networks
Language english
Publication Year 2020
HGF-reported in Year 2020
e-ISSN 1758-2946
Journal Journal of Cheminformatics
Quellenangaben Volume: 12, Issue: 1, Pages: , Article Number: 17 Supplement: ,
Publisher Bmc
Publishing Place Campus, 4 Crinan St, London N1 9xw, England
Reviewing status Peer reviewed
Institute(s) Institute of Structural Biology (STB)
POF-Topic(s) 30203 - Molecular Targets and Therapies
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503000-001
DOI 10.1186/s13321-020-00423-w
WOS ID WOS:000522102900001
Scopus ID 85083271107
PubMed ID 33431004
Erfassungsdatum 2020-04-15
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