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Transformer-CNN: Swiss knife for QSAR modeling and interpretation.
J. Cheminformatics 12:17 (2020)
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
e-ISSN
1758-2946
Journal
Journal of Cheminformatics
Quellenangaben
Volume: 12,
Issue: 1,
Article Number: 17
Publisher
BioMed Central
Publishing Place
Campus, 4 Crinan St, London N1 9xw, England
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Structural Biology (STB)