315 citations · 721 across the 6 of their papers we have counts for
6 papers
Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES
Esben Jannik Bjerrum, Christian Margreitter, Thomas Blaschke +1
Using generative deep learning models and reinforcement learning together can effectively generate new molecules with desired properties. By employing a multi-objective scoring fun…
Improving Chemical Autoencoder Latent Space and Molecular De novo Generation Diversity with Heteroencoders
Esben Jannik Bjerrum, Boris Sattarov
Chemical autoencoders are attractive models as they combine chemical space navigation with possibilities for de-novo molecule generation in areas of interest. This enables them to…
DeepIEP: a Peptide Sequence Model of Isoelectric Point (IEP/pI) using Recurrent Neural Networks (RNNs)
Esben Jannik Bjerrum
The isoelectric point (IEP or pI) is the pH where the net charge on the molecular ensemble of peptides and proteins is zero. This physical-chemical property is dependent on protona…
Data Augmentation of Spectral Data for Convolutional Neural Network (CNN) Based Deep Chemometrics
Esben Jannik Bjerrum, Mads Glahder, Thomas Skov
Deep learning methods are used on spectroscopic data to predict drug content in tablets from near infrared (NIR) spectra. Using convolutional neural networks (CNNs), features are e…
Molecular Generation with Recurrent Neural Networks (RNNs)
Esben Jannik Bjerrum, Richard Threlfall
The potential number of drug like small molecules is estimated to be between 10^23 and 10^60 while current databases of known compounds are orders of magnitude smaller with approxi…
SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules
Esben Jannik Bjerrum
Simplified Molecular Input Line Entry System (SMILES) is a single line text representation of a unique molecule. One molecule can however have multiple SMILES strings, which is a r…