Augmentation Is What You Need!

Tetko, Igor V.; Karpov, Pavel; Bruno, Eric; Kimber, Talia B.; Godin, Guillaume

doi:10.1007/978-3-030-30493-5_79

Cited by 41 publications

(33 citation statements)

References 6 publications

(6 reference statements)

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“…We used the same datasets (9 for regression and 9 for classification) that were exploited in our previous studies [11,22]. Short information about these sets as well as links to original works are provided in Table 1.…”

Section: Validation Datasetsmentioning

confidence: 99%

“…After the demonstration of text understanding from character-level inputs [17], this technique was adopted in chemoinformatics [11,[18][19][20][21]. Recently, we showed that the augmentation of SMILES (using canonical as well as non-canonical SMILES during model training and inference) increases the performance of convolutional models for regression and classification tasks [22].…”

Section: Introductionmentioning

confidence: 99%

“…We omitted the standard mean errors, which are 0.01 or less, for the reported values a Results from our previous study[22]. b Best performance calculated with CDDD descriptors obtained using autoencoder Sml2canSml from[27] Coefficient of determination, r 2 , calculated for regression sets (higher values are better)…”

mentioning

confidence: 99%

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Transformer-CNN: Swiss knife for QSAR modeling and interpretation

2020

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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 ://githu b.com/bigch em/trans forme r-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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Section: Validation Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Transformer-CNN: Swiss knife for QSAR modeling and interpretation

2020

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“…Augmentation was done offline prior to training the network. Randomized SMILES were generated using RDKit by setting option doRandom = True, which was recently introduced to improve regression and classification models for physico-chemical properties [22,23]. As expected, the augmentation improved the percentage of generated valid SMILES while lowering the number of training epochs.…”

Section: Table 2 Comparison Architectures a B C And Dmentioning

confidence: 83%

“…Our calculations based on the publicly available dataset PubChem [21], clearly demonstrate that the use of bidirectional layers systematically improves the capability of the GEN to generate a vast set of new SMILES within the property space of the training set. Following excellent results of SMILES augmentation for smaller datasets to predict physicochemical properties [22][23][24] and generators [25], we have used SMILES augmentation to increase both the number and diversity of SMILES in the training set.…”

Section: Introductionmentioning

confidence: 99%

GEN: highly efficient SMILES explorer using autodidactic generative examination networks

et al. 2020

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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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Quinoline Hydrazone Derivatives as New Antibacterials against Multidrug Resistant Strains

Hodyna

Kovalishyn

Romanenko

et al. 2023

Chemistry & Biodiversity

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To develop novel antimicrobial agents a series of 2(4)‐hydrazone derivatives of quinoline were designed, synthesized and tested. QSAR models of the antibacterial activity of quinoline derivatives were developed by the OCHEM web platform using different machine learning methods. A virtual set of quinoline derivatives was verified with a previously published classification model of anti‐E. coli activity and screened using the regression model of anti‐S. aureus activity. Selected and synthesized 2(4)‐hydrazone derivatives of quinoline exhibited antibacterial activity against the standard and antibiotic‐resistant S. aureus and E. coli strains in the range from 15 to 30 mm by the diameter of growth inhibition zones. Molecular docking showed the complex formation of the studied compounds into the catalytic domain of dihydrofolate reductase with an estimated binding affinity from ‐8.4 to ‐9.4 kcal/mol.

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Augmentation Is What You Need!

Cited by 41 publications

References 6 publications

Transformer-CNN: Swiss knife for QSAR modeling and interpretation

Transformer-CNN: Swiss knife for QSAR modeling and interpretation

GEN: highly efficient SMILES explorer using autodidactic generative examination networks

Quinoline Hydrazone Derivatives as New Antibacterials against Multidrug Resistant Strains

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