Ola Engkvist

Showing 35 publications

2025

Temporal Evaluation of Probability Calibration with Experimental Errors

Hannah Rosa Friesacher, Emma Svensson, Ádám Arany et al
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 14894 LNCS, p. 13-20
Paper in proceeding
2025

Towards Interpretable Models of Chemist Preferences for Human-in-the-Loop Assisted Drug Discovery

Yasmine Nahal, Markus Heinonen, Mikhail Kabeshov et al
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 14894 LNCS, p. 58-70
Paper in proceeding
2025

Temporal Evaluation of Uncertainty Quantification Under Distribution Shift

Emma Svensson, Hannah Rosa Friesacher, Ádám Arany et al
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 14894 LNCS, p. 132-148
Paper in proceeding
2025

Registries in Machine Learning-Based Drug Discovery: A Shortcut to Code Reuse

Peter B.R. Hartog, Emma Svensson, Lewis H. Mervin et al
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 14894 LNCS, p. 98-115
Paper in proceeding
2024

Scoring the synthesizability of any novel non-natural amino acid

Gökçe Geylan, Mikhail Kabeshov, Samuel Genheden et al
Journal of Peptide Science. Vol. 30 (Supplement 2), p. 412 (Meeting Abstract P2.276)-412
Other conference contribution
2024

Utilizing reinforcement learning for de novo drug design

Hampus Gummesson Svensson, Christian Tyrchan, Ola Engkvist et al
Machine Learning. Vol. 113 (7), p. 4811-4843
Journal article
2024

Generation of conformational ensembles of small molecules via surrogate model-assisted molecular dynamics

Juan Viguera Diez, Sara Romeo Atance, Ola Engkvist et al
Machine Learning: Science and Technology. Vol. 5 (2)
Journal article
2024

A call for an industry-led initiative to critically assess machine learning for real-world drug discovery

Cas Wognum, Jeremy R. Ash, Matteo Aldeghi et al
Nature Machine Intelligence. Vol. 6 (10), p. 1120-1121
Other text in scientific journal
2024

Evaluation of reinforcement learning in transformer-based molecular design

Jiazhen He, Alessandro Tibo, Jon Paul Janet et al
Journal of Cheminformatics. Vol. 16 (1)
Journal article
2024

Metis: a python-based user interface to collect expert feedback for generative chemistry models

Janosch Menke, Yasmine Nahal, Esben Jannik Bjerrum et al
Journal of Cheminformatics. Vol. 16 (1)
Journal article
2024

Exhaustive local chemical space exploration using a transformer model

Alessandro Tibo, Jiazhen He, Jon Paul Janet et al
Nature Communications. Vol. 15 (1)
Journal article
2024

Expanding the chemical space using a chemical reaction knowledge graph

Emma Rydholm, Tomas Bastys, Emma Svensson et al
Digital Discovery. Vol. 3 (7), p. 1378-1388
Journal article
2024

A methodology to correctly assess the applicability domain of cell membrane permeability predictors for cyclic peptides

Gökçe Geylan, Leonardo De Maria, Ola Engkvist et al
Digital Discovery. Vol. 3 (9), p. 1761-1775
Journal article
2024

QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design

Lewis H. Mervin, Alexey Voronov, Mikhail Kabeshov et al
Journal of Chemical Information and Modeling. Vol. 64 (14), p. 5365-5374
Journal article
2024

Navigating the Maize: cyclic and conditional computational graphs for molecular simulation

Thomas Lohr, Michele Assante, Michael Dodds et al
DIGITAL DISCOVERY. Vol. In Press
Journal article
2023

Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models

Jonathan G.M. Conn, James W. Carter, Justin J.A. Conn et al
Journal of Chemical Information and Modeling. Vol. 63 (4), p. 1099-1113
Journal article
2023

De novo generated combinatorial library design

Simon Johansson, Morteza Haghir Chehreghani, Ola Engkvist et al
Digital Discovery. Vol. 3 (1), p. 122-135
Journal article
2023

Industry-Scale Orchestrated Federated Learning for Drug Discovery

Martijn Oldenhof, Gergely Ács, Balázs Pejó et al
Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023. Vol. 37, p. 15576-15584
Paper in proceeding
2023

Link-INVENT: generative linker design with reinforcement learning

Jeff Guo, Franziska Knuth, Christian Margreitter et al
Digital Discovery. Vol. 2 (2), p. 392-408
Journal article
2022

Transformer-based molecular optimization beyond matched molecular pairs

Jiazhen He, Eva Nittinger, Christian Tyrchan et al
Journal of Cheminformatics. Vol. 14 (1)
Journal article
2022

A review of biomedical datasets relating to drug discovery: a knowledge graph perspective

Stephen Bonner, Ian P. Barrett, Cheng Ye et al
Briefings in Bioinformatics. Vol. In Press
Review article
2022

Autonomous Drug Design with Multi-Armed Bandits

Hampus Gummesson Svensson, Esben Jannik Bjerrum, Christian Tyrchan et al
Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, p. 5584-5592
Paper in proceeding
2022

Icolos: a workflow manager for structure-based post-processing of de novo generated small molecules

J. Harry Moore, Matthias R. Bauer, Jeff Guo et al
Bioinformatics. Vol. 38 (21), p. 4951-4952
Journal article
2022

Evaluation guidelines for machine learning tools in the chemical sciences

Andreas Bender, Nadine Schneider, Marwin Segler et al
Nature Reviews Chemistry. Vol. 6 (6), p. 428-442
Journal article
2022

De Novo Drug Design Using Reinforcement Learning with Graph- Based Deep Generative Models

Sara Romeo Atance, Juan Viguera Diez, Ola Engkvist et al
Journal of Chemical Information and Modeling. Vol. 62 (20), p. 4863-4872
Journal article
2022

Implications of topological imbalance for representation learning on biomedical knowledge graphs

Stephen Bonner, Ufuk Kirik, Ola Engkvist et al
Briefings in Bioinformatics. Vol. In Press
Journal article
2022

LibINVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design

Vendy Fialková, Jiaxi Zhao, Kostas Papadopoulos et al
Journal of Chemical Information and Modeling. Vol. 62 (9), p. 2046-2063
Journal article
2022

Exploring Graph Traversal Algorithms in Graph-Based Molecular Generation

Rocio Mercado, Esben Jannik Bjerrum, Ola Engkvist
Journal of Chemical Information and Modeling. Vol. 62 (9), p. 2093-2100
Journal article
2022

Human-in-the-loop assisted de novo molecular design

Iiris Sundin, Alexey Voronov, Haoping Xiao et al
Journal of Cheminformatics. Vol. 14 (1)
Journal article
2022

Improving de novo molecular design with curriculum learning

Jeff Guo, Vendy Fialkova, Juan Diego Arango et al
Nature Machine Intelligence. Vol. 4 (6), p. 555-563
Journal article
2022

Prediction of the Chemical Context for Buchwald-Hartwig Coupling Reactions

Samuel Genheden, Agnes Mårdh, Gustav Lahti et al
Molecular Informatics. Vol. 41 (8)
Journal article
2022

Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction

Simon Johansson, Hampus Gummesson Svensson, Esben Jannik Bjerrum et al
Molecular Informatics. Vol. 41 (12)
Journal article
2021

DockStream: a docking wrapper to enhance de novo molecular design

Jeff Guo, Jon Paul Janet, Matthias Bauer et al
Journal of Cheminformatics. Vol. 13 (1)
Journal article
2021

Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty

Lewis H. Mervin, Maria Anna Trapotsi, Avid M. Afzal et al
Journal of Cheminformatics. Vol. 13 (1)
Journal article
2021

Parallel Capsule Networks for Classification of White Blood Cells

Juan P. Vigueras-Guillén, Arijit Patra, Ola Engkvist et al
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 12907 LNCS, p. 743-752
Paper in proceeding

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