On the Relation between Retention Indexes and the Interaction between the Solute and the Column in Gas-Liquid Chromatography. - Ola Engkvist; Piotr Borowski; Agneta Bemgård; Gunnar Karlström; Roland Lindh; Anders Colmsjö (J. Chem. Inf. Comput. Sci., 1996)

Theoretical study of intermolecular potential energy surface for HCl dimer: Example of nonspherical atom-atom exchange repulsion interaction. - Jose Manuel Hermida-Ramón; Ola Engkvist; Gunnar Karlström (J. Comput. Chem., 1998)

High-Throughput, In Silico Prediction of Aqueous Solubility Based on One- and Two-Dimensional Descriptors. - Ola Engkvist; Paul Wrede (J. Chem. Inf. Comput. Sci., 2002)

Prediction of CNS Activity of Compound Libraries Using Substructure Analysis. - Ola Engkvist; Paul Wrede; Ulrich Rester (J. Chem. Inf. Comput. Sci., 2003)

Multifingerprint Based Similarity Searches for Targeted Class Compound Selection. - Thierry Kogej; Ola Engkvist; Niklas Blomberg; Sorel Muresan (J. Chem. Inf. Model., 2006)

ProSAR: A New Methodology for Combinatorial Library Design. - Hongming Chen 001; Ulf Borjesson; Ola Engkvist; Thierry Kogej; Mats Svensson; Niklas Blomberg; Dirk Weigelt; Jeremy N. Burrows; Tim Lange 002 (J. Chem. Inf. Model., 2009)

Comparison of Molecular Fingerprint Methods on the Basis of Biological Profile Data. - Andreas Steffen; Thierry Kogej; Christian Tyrchan; Ola Engkvist (J. Chem. Inf. Model., 2009)

Molecular Topology Analysis of the Differences between Drugs, Clinical Candidate Compounds, and Bioactive Molecules. - Hongming Chen 001; Yidong Yang; Ola Engkvist (J. Chem. Inf. Model., 2010)

HTS explorer. - Christoph Müller 003; Isabella Feierberg; Ola Engkvist; Christian Tyrchan (J. Cheminformatics, 2014)

Hit series selection in noisy HTS data: clustering techniques, statistical tests and data visualisations. - Christoph Müller 003; Daniel L. Ormsby; Isabella Feierberg; Ola Engkvist; Christian Tyrchan; Michael J. Hartshorn (J. Cheminformatics, 2014)

Ligand-Based Target Prediction with Signature Fingerprints. - Jonathan Alvarsson; Martin Eklund; Ola Engkvist; Ola Spjuth; Lars Carlsson; Jarl E. S. Wikberg; Tobias Noeske (J. Chem. Inf. Model., 2014)

Target prediction utilising negative bioactivity data covering large chemical space. - Lewis H. Mervin; Avid M. Afzal; Georgios Drakakis; Richard Lewis 002; Ola Engkvist; Andreas Bender 002 (J. Cheminformatics, 2015)

Investigating Pharmacological Similarity by Charting Chemical Space. - Rosa Buonfiglio; Ola Engkvist; Péter L. Várkonyi; Astrid Henz; Elisabet Vikeved; Anders Backlund; Thierry Kogej (J. Chem. Inf. Model., 2015)

Using Conformal Prediction to Prioritize Compound Synthesis in Drug Discovery. - Ernst Ahlberg; Susanne Winiwarter; Henrik Boström; Henrik Linusson; Tuve Löfström; Ulf Norinder; Ulf Johansson; Ola Engkvist; Oscar Hammar; Claus Bendtsen; Lars Carlsson (COPA, 2017)

Application of generative autoencoder in de novo molecular design. - Thomas Blaschke; Marcus Olivecrona; Ola Engkvist; Jürgen Bajorath; Hongming Chen 001 (CoRR, 2017)

Molecular de-novo design through deep reinforcement learning. - Marcus Olivecrona; Thomas Blaschke; Ola Engkvist; Hongming Chen 001 (J. Cheminformatics, 2017)

ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics. - Jiangming Sun; Nina Jeliazkova; Vladimir I. Chupakhin; José Felipe Golib Dzib; Ola Engkvist; Lars Carlsson; Jörg K. Wegner; Hugo Ceulemans; Ivan Georgiev; Vedrin Jeliazkov; Nikolay T. Kochev; Thomas J. Ashby; et al. (J. Cheminformatics, 2017)

Erratum to: ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics. - Jiangming Sun; Nina Jeliazkova; Vladimir I. Chupakhin; José Felipe Golib Dzib; Ola Engkvist; Lars Carlsson; Jörg K. Wegner; Hugo Ceulemans; Ivan Georgiev; Vedrin Jeliazkov; Nikolay T. Kochev; Thomas J. Ashby; et al. (J. Cheminformatics, 2017)

Applying Mondrian Cross-Conformal Prediction To Estimate Prediction Confidence on Large Imbalanced Bioactivity Data Sets. - Jiangming Sun; Lars Carlsson; Ernst Ahlberg; Ulf Norinder; Ola Engkvist; Hongming Chen 001 (J. Chem. Inf. Model., 2017)

Innovation in Small-Molecule-Druggable Chemical Space: Where are the Initial Modulators of New Targets Published? - Stephanie K. Ashenden; Thierry Kogej; Ola Engkvist; Andreas Bender 002 (J. Chem. Inf. Model., 2017)

Venn-Abers predictors for improved compound iterative screening in drug discovery. - Rubén Buendía; Ola Engkvist; Lars Carlsson; Thierry Kogej; Ernst Ahlberg (COPA, 2018)

Orthologue chemical space and its influence on target prediction. - Lewis H. Mervin; Krishna C. Bulusu; Leen Kalash; Avid M. Afzal; Fredrik Svensson; Mike A. Firth; Ian P. Barrett; Ola Engkvist; Andreas Bender 002 (Bioinform., 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)

Attention and Edge Memory Convolution for Bioactivity Prediction. - Michael Withnall; Edvard Lindelöf; Ola Engkvist; Hongming Chen 001 (ICANN (Workshop), 2019)

Exploring the GDB-13 chemical space using deep generative models. - Josep Arús-Pous; Thomas Blaschke; Silas Ulander; Jean-Louis Reymond; Hongming Chen 001; Ola Engkvist (J. Cheminformatics, 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)

Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability. - Oliver Laufkötter; Noé Sturm; Jürgen Bajorath; 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)

Accurate Hit Estimation for Iterative Screening Using Venn-ABERS Predictors. - Rubén Buendía; Thierry Kogej; Ola Engkvist; Lars Carlsson; Henrik Linusson; Ulf Johansson; Paolo Toccaceli; Ernst Ahlberg (J. Chem. Inf. Model., 2019)

Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models. - Noé Sturm; Jiangming Sun; Yves Vandriessche; Andreas Mayr; Günter Klambauer; Lars Carlsson; Ola Engkvist; Hongming Chen 001 (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)

Molecular representations in AI-driven drug discovery: a review and practical guide. - Laurianne David; Amol Thakkar; Rocío Mercado; 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)

Industry-scale application and evaluation of deep learning for drug target prediction. - Noé Sturm; Andreas Mayr; Thanh Le Van; Vladimir I. Chupakhin; Hugo Ceulemans; Jörg K. Wegner; José Felipe Golib Dzib; Nina Jeliazkova; Yves Vandriessche; Stanislav Böhm; Vojtech Cima; Jan Martinovic; et al. (J. Cheminformatics, 2020)

From Big Data to Artificial Intelligence: chemoinformatics meets new challenges. - Igor V. Tetko; Ola Engkvist (J. Cheminformatics, 2020)

Building attention and edge message passing neural networks for bioactivity and physical-chemical property prediction. - Michael Withnall; Edvard Lindelöf; Ola Engkvist; Hongming Chen 001 (J. Cheminformatics, 2020)

REINVENT 2.0: An AI Tool for De Novo Drug Design. - Thomas Blaschke; Josep Arús-Pous; Hongming Chen 001; Christian Margreitter; Christian Tyrchan; Ola Engkvist; Kostas Papadopoulos; Atanas Patronov (J. Chem. Inf. Model., 2020)

Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Protein-Ligand Predictions. - Lewis H. Mervin; Avid M. Afzal; Ola Engkvist; Andreas Bender 002 (J. Chem. Inf. Model., 2020)

Multisolvent Models for Solvation Free Energy Predictions Using 3D-RISM Hydration Thermodynamic Descriptors. - Vigneshwari Subramanian; Ekaterina Ratkova; David Palmer 002; Ola Engkvist; Maxim V. Fedorov; Antonio Llinàs (J. Chem. Inf. Model., 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)

Parallel Capsule Networks for Classification of White Blood Cells. - Juan P. Vigueras-Guillén; Arijit Patra; Ola Engkvist; Frank Seeliger (MICCAI (7), 2021)

A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective. - Stephen Bonner; Ian P. Barrett; Cheng Ye 002; Rowan Swiers; Ola Engkvist; Andreas Bender 002; William L. Hamilton (CoRR, 2021)

Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery. - Stephen Bonner; Ian P. Barrett; Cheng Ye 002; Rowan Swiers; Ola Engkvist; William L. Hamilton (CoRR, 2021)

MAIP: a web service for predicting blood-stage malaria inhibitors. - Nicolas Bosc; Eloy Felix; Ricardo Arcila; David Mendez; Martin R. Saunders; Darren V. S. Green; Jason Ochoada; Anang A. Shelat; Eric J. Martin; Preeti Iyer; Ola Engkvist; Andreas Verras; et al. (J. Cheminformatics, 2021)

DockStream: a docking wrapper to enhance de novo molecular design. - Jeff Guo; Jon Paul Janet; Matthias R. Bauer; Eva Nittinger; Kathryn A. Giblin; Kostas Papadopoulos; Alexey Voronov; Atanas Patronov; Ola Engkvist; Christian Margreitter (J. Cheminformatics, 2021)

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)

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; Ian P. Barrett; Andreas Bender 002; 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)

Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions. - Maria-Anna Trapotsi; Lewis H. Mervin; Avid M. Afzal; Noé Sturm; Ola Engkvist; Ian P. Barrett; Andreas Bender 002 (J. Chem. Inf. Model., 2021)

Comparative Study of Deep Generative Models on Chemical Space Coverage. - Jie Zhang 089; Rocío Mercado; Ola Engkvist; Hongming Chen 001 (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)

A review of biomedical datasets relating to drug discovery: a knowledge graph perspective. - Stephen Bonner; Ian P. Barrett; Cheng Ye 002; Rowan Swiers; Ola Engkvist; Andreas Bender 002; Charles Tapley Hoyt; William L. Hamilton (Briefings Bioinform., 2022)

Implications of topological imbalance for representation learning on biomedical knowledge graphs. - Stephen Bonner; Ufuk Kirik; Ola Engkvist; Jian Tang; Ian P. Barrett (Briefings Bioinform., 2022)

Industry-Scale Orchestrated Federated Learning for Drug Discovery. - Martijn Oldenhof; Gergely Ács; Balázs Pejó; Ansgar Schuffenhauer; Nicholas Holway; Noé Sturm; Arne Dieckmann; Oliver Fortmeier; Eric Boniface; Clément Mayer; Arnaud Gohier; Peter Schmidtke; et al. (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)

De Novo Drug Design Using Reinforcement Learning with Graph-Based Deep Generative Models. - Sara Romeo Atance; Juan Viguera Diez; Ola Engkvist; Simon Olsson; Rocío Mercado (J. Chem. Inf. Model., 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)

Improving de novo molecular design with curriculum learning. - Jeff Guo; Vendy Fialková; Juan Diego Arango; Christian Margreitter; Jon Paul Janet; Kostas Papadopoulos; Ola Engkvist; Atanas Patronov (Nat. Mach. Intell., 2022)

Author Correction: Improving de novo molecular design with curriculum learning. - Jeff Guo; Vendy Fialková; Juan Diego Arango; Christian Margreitter; Jon Paul Janet; Kostas Papadopoulos; Ola Engkvist; Atanas Patronov (Nat. Mach. Intell., 2022)

Icolos: a workflow manager for structure-based post-processing of de novo generated small molecules. - J. Harry Moore; Matthias R. Bauer; Jeff Guo; Atanas Patronov; Ola Engkvist; Christian Margreitter (Bioinform., 2022)

Industry-Scale Orchestrated Federated Learning for Drug Discovery. - Martijn Oldenhof; Gergely Ács; Balázs Pejó; Ansgar Schuffenhauer; Nicholas Holway; Noé Sturm; Arne Dieckmann; Oliver Fortmeier; Eric Boniface; Clément Mayer; Arnaud Gohier; Peter Schmidtke; et al. (AAAI, 2023)

Diverse Data Expansion with Semi-Supervised k-Determinantal Point Processes. - Simon Johansson; Ola Engkvist; Morteza Haghir Chehreghani; Alexander Schliep (IEEE Big Data, 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; Vigneshwari Subramanian; Andrew Baxter; Ola Engkvist; Antonio Llinàs; Ekaterina Ratkova; Stephen D. Pickett; James L. McDonagh; David S. Palmer (J. Chem. Inf. Model., 2023)

AiZynthTrain: Robust, Reproducible, and Extensible Pipelines for Training Synthesis Prediction Models. - Samuel Genheden; Per-Ola Norrby; Ola Engkvist (J. Chem. Inf. Model., 2023)

Temporal Evaluation of Probability Calibration with Experimental Errors. - Hannah Rosa Friesacher; Emma Svensson; Adam Arany; Lewis H. Mervin; Ola Engkvist (AIDD@ICANN, 2024)

Registries in Machine Learning-Based Drug Discovery: A Shortcut to Code Reuse. - Peter B. R. Hartog; Emma Svensson; Lewis H. Mervin; Samuel Genheden; Ola Engkvist; Igor V. Tetko (AIDD@ICANN, 2024)

Leveraging Quantum Mechanical Properties to Predict Solvent Effects on Large Drug-Like Molecules. - Mathias Hilfiker; Leonardo Medrano Sandonas; Marco Klähn; Ola Engkvist; Alexandre Tkatchenko (AIDD@ICANN, 2024)

Towards Interpretable Models of Chemist Preferences for Human-in-the-Loop Assisted Drug Discovery. - Yasmine Nahal; Markus Heinonen; Mikhail Kabeshov; Jon Paul Janet; Eva Nittinger; Ola Engkvist; Samuel Kaski (AIDD@ICANN, 2024)

Temporal Evaluation of Uncertainty Quantification Under Distribution Shift. - Emma Svensson; Hannah Rosa Friesacher; Adam Arany; Lewis H. Mervin; Ola Engkvist (AIDD@ICANN, 2024)

Navigating the Maize: Cyclic and conditional computational graphs for molecular simulation. - Thomas Löhr; Michael Dodds; Lili Cao; Mikhail Kabeshov; Michele Assante; Jon Paul Janet; Marco Klähn; Ola Engkvist (CoRR, 2024)

Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels. - Emma Svensson; Hannah Rosa Friesacher; Susanne Winiwarter; Lewis H. Mervin; Adam Arany; Ola Engkvist (CoRR, 2024)

PepINVENT: Generative peptide design beyond the natural amino acids. - Gökçe Geylan; Jon Paul Janet; Alessandro Tibo; Jiazhen He; Atanas Patronov; Mikhail Kabeshov; Florian David; Werngard Czechtizky; Ola Engkvist; Leonardo De Maria (CoRR, 2024)

Diversity-Aware Reinforcement Learning for de novo Drug Design. - Hampus Gummesson Svensson; Christian Tyrchan; Ola Engkvist; Morteza Haghir Chehreghani (CoRR, 2024)

MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information. - Wouter Heyndrickx; Lewis H. Mervin; Tobias Morawietz; Noé Sturm; Lukas Friedrich; Adam Zalewski; Anastasia Pentina; Lina Humbeck; Martijn Oldenhof; Ritsuya Niwayama; Peter Schmidtke; Nikolas Fechner; et al. (J. Chem. Inf. Model., 2024)

QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design. - Lewis H. Mervin; Alexey Voronov; Mikhail Kabeshov; Ola Engkvist (J. Chem. Inf. Model., 2024)

Generation of conformational ensembles of small molecules via surrogate model-assisted molecular dynamics. - Juan Viguera Diez; Sara Romeo Atance; Ola Engkvist; Simon Olsson (Mach. Learn. Sci. Technol., 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; Raquel Rodríguez-Pérez; Cheng Fang; Alan C. Cheng; Daniel J. Price; Djork-Arné Clevert; Ola Engkvist; W. Patrick Walters (Nat. Mac. Intell., 2024)

Utilizing reinforcement learning for de novo drug design. - Hampus Gummesson Svensson; Christian Tyrchan; Ola Engkvist; Morteza Haghir Chehreghani (Mach. Learn., 2024)

Evaluation of reinforcement learning in transformer-based molecular design. - Jiazhen He; Alessandro Tibo; Jon Paul Janet; Eva Nittinger; Christian Tyrchan; Werngard Czechtizky; Ola Engkvist (J. Cheminformatics, 2024)

Reinvent 4: Modern AI-driven generative molecule design. - Hannes H. Loeffler; Jiazhen He; Alessandro Tibo; Jon Paul Janet; Alexey Voronov; Lewis H. Mervin; Ola Engkvist (J. Cheminformatics, 2024)

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)

Human-in-the-loop active learning for goal-oriented molecule generation. - Yasmine Nahal; Janosch Menke; Julien Martinelli; Markus Heinonen; Mikhail Kabeshov; Jon Paul Janet; Eva Nittinger; Ola Engkvist; Samuel Kaski (J. Cheminformatics, 2024)

AiZynthFinder 4.0: developments based on learnings from 3 years of industrial application. - Lakshidaa Saigiridharan; Alan Kai Hassen; Helen Lai; Paula Torren-Peraire; Ola Engkvist; Samuel Genheden (J. Cheminformatics, 2024)

Boltzmann priors for Implicit Transfer Operators. - Juan Viguera Diez; Mathias Jacob Schreiner; Ola Engkvist; Simon Olsson (ICLR, 2025)

Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models. - Hannah Rosa Friesacher; Emma Svensson; Susanne Winiwarter; Lewis H. Mervin; Adam Arany; Ola Engkvist (CoRR, 2025)

Diverse Mini-Batch Selection in Reinforcement Learning for Efficient Chemical Exploration in de novo Drug Design. - Hampus Gummesson Svensson; Ola Engkvist; Jon Paul Janet; Christian Tyrchan; Morteza Haghir Chehreghani (CoRR, 2025)

Exploring Modularity of Agentic Systems for Drug Discovery. - Laura van Weesep; Samuel Genheden; Ola Engkvist; Jens Sjölund (CoRR, 2025)

Achieving well-informed decision-making in drug discovery: a comprehensive calibration study using neural network-based structure-activity models. - Hannah Rosa Friesacher; Ola Engkvist; Lewis H. Mervin; Yves Moreau; Adam Arany (J. Cheminformatics, 2025)

Publishing neural networks in drug discovery might compromise training data privacy. - Fabian Krüger; Johan Östman; Lewis H. Mervin; Igor V. Tetko; Ola Engkvist (J. Cheminformatics, 2025)

E-GuARD: expert-guided augmentation for the robust detection of compounds interfering with biological assays. - Vincenzo Palmacci; Yasmine Nahal; Matthias Welsch; Ola Engkvist; Samuel Kaski; Johannes Kirchmair (J. Cheminformatics, 2025)