Synergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular Prediction. - Talia B. Kimber; Sebastian Engelke; Igor V. Tetko; Eric Bruno; Guillaume Godin (CoRR, 2018)

A Transformer Model for Retrosynthesis. - Pavel Karpov; Guillaume Godin; Igor V. Tetko (ICANN (Workshop), 2019)

Augmentation Is What You Need! - Igor V. Tetko; Pavel Karpov; Eric Bruno; Talia B. Kimber; Guillaume Godin (ICANN (Workshop), 2019)

Deep Generative Model for Sparse Graphs using Text-Based Learning with Augmentation in Generative Examination Networks. - Ruud van Deursen; Guillaume Godin (CoRR, 2019)

Multitask Learning On Graph Neural Networks Applied To Molecular Property Predictions. - Fabio Capela; Vincent Nouchi; Ruud van Deursen; Igor V. Tetko; Guillaume Godin (CoRR, 2019)

Transformer-CNN: Fast and Reliable tool for QSAR. - Pavel Karpov; Guillaume Godin; Igor V. Tetko (CoRR, 2019)

Augmented Transformer Achieves 97% and 85% for Top5 Prediction of Direct and Classical Retro-Synthesis. - Igor V. Tetko; Pavel Karpov; Ruud van Deursen; Guillaume Godin (CoRR, 2020)

Beyond Chemical 1D knowledge using Transformers. - Ruud van Deursen; Igor V. Tetko; Guillaume Godin (CoRR, 2020)

GEN: highly efficient SMILES explorer using autodidactic generative examination networks. - Ruud van Deursen; Peter Ertl; Igor V. Tetko; Guillaume Godin (J. Cheminformatics, 2020)

Transformer-CNN: Swiss knife for QSAR modeling and interpretation. - Pavel Karpov; Guillaume Godin; Igor V. Tetko (J. Cheminformatics, 2020)

Be aware of overfitting by hyperparameter optimization! - Igor V. Tetko; Ruud van Deursen; Guillaume Godin (J. Cheminformatics, 2024)

All You Need Is Synthetic Task Augmentation. - Guillaume Godin (CoRR, 2025)

Z-Error Loss for Training Neural Networks. - Guillaume Godin (CoRR, 2025)