34 citations · 34 across the 1 of their papers we have counts for
2 papers
cs.LG2019
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…
cs.LG2018★ 34 cited
In silico generation of novel, drug-like chemical matter using the LSTM neural network
Peter Ertl, Richard Lewis, Eric Martin +1
The exploration of novel chemical spaces is one of the most important tasks of cheminformatics when supporting the drug discovery process. Properly designed and trained deep neural…