ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction

By Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan Oken Hodas. 2017

21 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Garrett B. Goh
2. Charles Siegel
3. Nathan Oken Hodas
4. Abhinav Vishnu
5. Martin Simonovsky
6. Tânia F Cova
7. Wei Ma
8. Chaopeng Shen

Citations in the corpus

• GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders (2018, neutral): “Our autoencoder might also be used to pre-train graph encoders for fine-tuning on small datasets (Goh et al., 2017).”
• SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties (2017, methodology): “Using a recently developed pre-training approach, [15] we were also able to improve the performance of SMILES2vec slightly, attaining AUC of 0.81 for the full Tox21 dataset, AUC of 0.80 for the HIV dataset, and RMSE 1.2 kcal/mol for the FreeSolv dataset.”