350 citations · 675 across the 7 of their papers we have counts for
4 papers · 1 filter
Transformer-CNN: Fast and Reliable tool for QSAR
Pavel Karpov, Guillaume Godin, Igor V. Tetko
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 upo…
Multitask Learning On Graph Neural Networks Applied To Molecular Property Predictions
Fabio Capela, Vincent Nouchi, Ruud Van Deursen +2
Prediction of molecular properties, including physico-chemical properties, is a challenging task in chemistry. Herein we present a new state-of-the-art multitask prediction method…
Deep Generative Model for Sparse Graphs using Text-Based Learning with Augmentation in Generative Examination Networks
Ruud van Deursen, Guillaume Godin
Graphs and networks are a key research tool for a variety of science fields, most notably chemistry, biology, engineering and social sciences. Modeling and generation of graphs wit…
GEN: Highly Efficient SMILES Explorer Using Autodidactic Generative Examination Networks
Ruud van Deursen, Peter Ertl, Igor V. Tetko +1
Recurrent neural networks have been widely used to generate millions of de novo molecules in a known chemical space. These deep generative models are typically setup with LSTM or G…