Machine learning optimization of cross docking accuracy. - Esben Jannik Bjerrum (Comput. Biol. Chem., 2016)

SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules. - Esben Jannik Bjerrum (CoRR, 2017)

Molecular Generation with Recurrent Neural Networks (RNNs). - Esben Jannik Bjerrum; Richard Threlfall (CoRR, 2017)

Data Augmentation of Spectral Data for Convolutional Neural Network (CNN) Based Deep Chemometrics. - Esben Jannik Bjerrum; Mads Glahder; Thomas Skov (CoRR, 2017)

DeepIEP: a Peptide Sequence Model of Isoelectric Point (IEP/pI) using Recurrent Neural Networks (RNNs). - Esben Jannik Bjerrum (CoRR, 2017)

pICalculax: Improved Prediction of Isoelectric Point for Modified Peptides. - Esben Jannik Bjerrum; Jan H. Jensen; Jakob L. Tolborg (J. Chem. Inf. Model., 2017)

Improving Chemical Autoencoder Latent Space and Molecular De novo Generation Diversity with Heteroencoders. - Esben Jannik Bjerrum (CoRR, 2018)

Improving Deep Generative Models with Randomized SMILES. - Josep Arús-Pous; Simon Johansson; Oleksii Prykhodko; Esben Jannik Bjerrum; Christian Tyrchan; Jean-Louis Reymond; Hongming Chen 001; Ola Engkvist (ICANN (Workshop), 2019)

Neural Network Guided Tree-Search Policies for Synthesis Planning. - Amol Thakkar; Esben Jannik Bjerrum; Ola Engkvist; Jean-Louis Reymond (ICANN (Workshop), 2019)

Randomized SMILES strings improve the quality of molecular generative models. - Josep Arús-Pous; Simon Johansson; Oleksii Prykhodko; Esben Jannik Bjerrum; Christian Tyrchan; Jean-Louis Reymond; Hongming Chen 001; Ola Engkvist (J. Cheminformatics, 2019)

A de novo molecular generation method using latent vector based generative adversarial network. - Oleksii Prykhodko; Simon Johansson; Panagiotis-Christos Kotsias; Josep Arús-Pous; Esben Jannik Bjerrum; Ola Engkvist; Hongming Chen 001 (J. Cheminformatics, 2019)

De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping. - Boris Sattarov; Igor I. Baskin; Dragos Horvath; Gilles Marcou; Esben Jannik Bjerrum; Alexandre Varnek (J. Chem. Inf. Model., 2019)

SMILES-based deep generative scaffold decorator for de-novo drug design. - Josep Arús-Pous; Atanas Patronov; Esben Jannik Bjerrum; Christian Tyrchan; Jean-Louis Reymond; Hongming Chen 001; Ola Engkvist (J. Cheminformatics, 2020)

AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning. - Samuel Genheden; Amol Thakkar; Veronika Chadimová; Jean-Louis Reymond; Ola Engkvist; Esben Jannik Bjerrum (J. Cheminformatics, 2020)

Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks. - Panagiotis-Christos Kotsias; Josep Arús-Pous; Hongming Chen 001; Ola Engkvist; Christian Tyrchan; Esben Jannik Bjerrum (Nat. Mach. Intell., 2020)

Molecular optimization by capturing chemist's intuition using deep neural networks. - Jiazhen He; Huifang You; Emil Sandström; Eva Nittinger; Esben Jannik Bjerrum; Christian Tyrchan; Werngard Czechtizky; Ola Engkvist (J. Cheminformatics, 2021)

Clustering of Synthetic Routes Using Tree Edit Distance. - Samuel Genheden; Ola Engkvist; Esben Jannik Bjerrum (J. Chem. Inf. Model., 2021)

Artificial applicability labels for improving policies in retrosynthesis prediction. - Esben Jannik Bjerrum; Amol Thakkar; Ola Engkvist (Mach. Learn. Sci. Technol., 2021)

Graph networks for molecular design. - Rocío Mercado; Tobias Rastemo; Edvard Lindelöf; Günter Klambauer; Ola Engkvist; Hongming Chen 001; Esben Jannik Bjerrum (Mach. Learn. Sci. Technol., 2021)

Autonomous Drug Design with Multi-Armed Bandits. - Hampus Gummesson Svensson; Esben Jannik Bjerrum; Christian Tyrchan; Ola Engkvist; Morteza Haghir Chehreghani (IEEE Big Data, 2022)

Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES. - Esben Jannik Bjerrum; Christian Margreitter; Thomas Blaschke; Raquel Lopez-Rios de Castro (CoRR, 2022)

Transformer-based molecular optimization beyond matched molecular pairs. - Jiazhen He; Eva Nittinger; Christian Tyrchan; Werngard Czechtizky; Atanas Patronov; Esben Jannik Bjerrum; Ola Engkvist (J. Cheminformatics, 2022)

Human-in-the-loop assisted de novo molecular design. - Iiris Sundin; Alexey Voronov; Haoping Xiao; Kostas Papadopoulos; Esben Jannik Bjerrum; Markus Heinonen; Atanas Patronov; Samuel Kaski; Ola Engkvist (J. Cheminformatics, 2022)

LibINVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design. - Vendy Fialková; Jiaxi Zhao; Kostas Papadopoulos; Ola Engkvist; Esben Jannik Bjerrum; Thierry Kogej; Atanas Patronov (J. Chem. Inf. Model., 2022)

Exploring Graph Traversal Algorithms in Graph-Based Molecular Generation. - Rocío Mercado; Esben Jannik Bjerrum; Ola Engkvist (J. Chem. Inf. Model., 2022)

Fast prediction of distances between synthetic routes with deep learning. - Samuel Genheden; Ola Engkvist; Esben Jannik Bjerrum (Mach. Learn. Sci. Technol., 2022)

Chemformer: a pre-trained transformer for computational chemistry. - Ross Irwin; Spyridon Dimitriadis; Jiazhen He; Esben Jannik Bjerrum (Mach. Learn. Sci. Technol., 2022)

Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES. - Esben Jannik Bjerrum; Christian Margreitter; Thomas Blaschke; Simona Kolarova; Raquel Lopez-Rios de Castro (J. Comput. Aided Mol. Des., 2023)

Metis: a python-based user interface to collect expert feedback for generative chemistry models. - Janosch Menke; Yasmine Nahal; Esben Jannik Bjerrum; Mikhail Kabeshov; Samuel Kaski; Ola Engkvist (J. Cheminformatics, 2024)