Ola Engkvist

Publication Activity (10 Years)

Years Active: 1996-2024
Publications (10 Years): 73

Top Topics

Reinforcement Learning

Conformal Prediction

Top Venues

J. Cheminformatics

J. Chem. Inf. Model.

Mach. Learn. Sci. Technol.

Publications

Juan Viguera Diez, Sara Romeo Atance, Ola Engkvist, Simon Olsson
Generation of conformational ensembles of small molecules via surrogate model-assisted molecular dynamics. Mach. Learn. Sci. Technol. 5 (2) (2024)
Hampus Gummesson Svensson, Christian Tyrchan, Ola Engkvist, Morteza Haghir Chehreghani
Utilizing reinforcement learning for de novo drug design. Mach. Learn. 113 (7) (2024)
Hannah Rosa Friesacher, Ola Engkvist, Lewis H. Mervin, Yves Moreau, Adam Arany
Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models. CoRR (2024)
Thomas Löhr, Michael Dodds, Lili Cao, Mikhail Kabeshov, Michele Assante, Jon Paul Janet, Marco Klähn, Ola Engkvist
Navigating the Maize: Cyclic and conditional computational graphs for molecular simulation. CoRR (2024)
Jiazhen He, Alessandro Tibo, Jon Paul Janet, Eva Nittinger, Christian Tyrchan, Werngard Czechtizky, Ola Engkvist
Evaluation of reinforcement learning in transformer-based molecular design. J. Cheminformatics 16 (1) (2024)
Janosch Menke, Yasmine Nahal, Esben Jannik Bjerrum, Mikhail Kabeshov, Samuel Kaski, Ola Engkvist
Metis: a python-based user interface to collect expert feedback for generative chemistry models. J. Cheminformatics 16 (1) (2024)
Lakshidaa Saigiridharan, Alan Kai Hassen, Helen Lai, Paula Torren-Peraire, Ola Engkvist, Samuel Genheden
AiZynthFinder 4.0: developments based on learnings from 3 years of industrial application. J. Cheminformatics 16 (1) (2024)
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, Jaak Simm, Adam Arany, Nicolas Drizard, Rama Jabal, Arina Afanasyeva, Regis Loeb, Shlok Verma, Simon Harnqvist, Matthew Holmes, Balazs Pejo, Maria Telenczuk, Nicholas Holway, Arne Dieckmann, Nicola Rieke, Friederike Zumsande, Djork-Arné Clevert, Michael Krug, Christopher N. Luscombe, Darren V. S. Green, Peter Ertl, Peter Antal, David Marcus, Nicolas Do Huu, Hideyoshi Fuji, Stephen D. Pickett, Gergely Ács, Eric Boniface, Bernd Beck, Yax Sun, Arnaud Gohier, Friedrich Rippmann, Ola Engkvist, Andreas H. Göller, Yves Moreau, Mathieu N. Galtier, Ansgar Schuffenhauer, Hugo Ceulemans
MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information. J. Chem. Inf. Model. 64 (7) (2024)
Lewis H. Mervin, Alexey Voronov, Mikhail Kabeshov, Ola Engkvist
QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design. J. Chem. Inf. Model. 64 (14) (2024)
Martijn Oldenhof, Gergely Ács, Balázs Pejó, Ansgar Schuffenhauer, Nicholas Holway, Noé Sturm, Arne Dieckmann, Oliver Fortmeier, Eric Boniface, Clément Mayer, Arnaud Gohier, Peter Schmidtke, Ritsuya Niwayama, Dieter Kopecky, Lewis H. Mervin, Prakash Chandra Rathi, Lukas Friedrich, András Formanek, Peter Antal, Jordon Rahaman, Adam Zalewski, Wouter Heyndrickx, Ezron Oluoch, Manuel Stößel, Michal Vanco, David Endico, Fabien Gelus, Thaïs de Boisfossé, Adrien Darbier, Ashley Nicollet, Matthieu Blottière, Maria Telenczuk, Van Tien Nguyen, Thibaud Martinez, Camille Boillet, Kelvin Moutet, Alexandre Picosson, Aurélien Gasser, Inal Djafar, Antoine Simon, Adam Arany, Jaak Simm, Yves Moreau, Ola Engkvist, Hugo Ceulemans, Camille Marini, Mathieu Galtier
Industry-Scale Orchestrated Federated Learning for Drug Discovery. AAAI (2023)
Samuel Genheden, Per-Ola Norrby, Ola Engkvist
AiZynthTrain: Robust, Reproducible, and Extensible Pipelines for Training Synthesis Prediction Models. J. Chem. Inf. Model. 63 (7) (2023)
Simon Johansson, Ola Engkvist, Morteza Haghir Chehreghani, Alexander Schliep
Diverse Data Expansion with Semi-Supervised k-Determinantal Point Processes. BigData (2023)
Hampus Gummesson Svensson, Christian Tyrchan, Ola Engkvist, Morteza Haghir Chehreghani
Utilizing Reinforcement Learning for de novo Drug Design. CoRR (2023)
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
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. J. Chem. Inf. Model. 63 (4) (2023)
Rocío Mercado, Esben Jannik Bjerrum, Ola Engkvist
Exploring Graph Traversal Algorithms in Graph-Based Molecular Generation. J. Chem. Inf. Model. 62 (9) (2022)
Martijn Oldenhof, Gergely Ács, Balázs Pejó, Ansgar Schuffenhauer, Nicholas Holway, Noé Sturm, Arne Dieckmann, Oliver Fortmeier, Eric Boniface, Clément Mayer, Arnaud Gohier, Peter Schmidtke, Ritsuya Niwayama, Dieter Kopecky, Lewis H. Mervin, Prakash Chandra Rathi, Lukas Friedrich, András Formanek, Peter Antal, Jordon Rahaman, Adam Zalewski, Ezron Oluoch, Manuel Stößel, Michal Vanco, David Endico, Fabien Gelus, Thaïs de Boisfossé, Adrien Darbier, Ashley Nicollet, Matthieu Blottière, Maria Telenczuk, Van Tien Nguyen, Thibaud Martinez, Camille Boillet, Kelvin Moutet, Alexandre Picosson, Aurélien Gasser, Inal Djafar, Adam Arany, Jaak Simm, Yves Moreau, Ola Engkvist, Hugo Ceulemans, Camille Marini, Mathieu Galtier
Industry-Scale Orchestrated Federated Learning for Drug Discovery. CoRR (2022)
Hampus Gummesson Svensson, Esben Jannik Bjerrum, Christian Tyrchan, Ola Engkvist, Morteza Haghir Chehreghani
Autonomous Drug Design with Multi-armed Bandits. CoRR (2022)
Jeff Guo, Vendy Fialková, Juan Diego Arango, Christian Margreitter, Jon Paul Janet, Kostas Papadopoulos, Ola Engkvist, Atanas Patronov
Improving de novo molecular design with curriculum learning. Nat. Mach. Intell. 4 (6) (2022)
Jeff Guo, Vendy Fialková, Juan Diego Arango, Christian Margreitter, Jon Paul Janet, Kostas Papadopoulos, Ola Engkvist, Atanas Patronov
Author Correction: Improving de novo molecular design with curriculum learning. Nat. Mach. Intell. 4 (8) (2022)
Samuel Genheden, Ola Engkvist, Esben Jannik Bjerrum
Fast prediction of distances between synthetic routes with deep learning. Mach. Learn. Sci. Technol. 3 (1) (2022)
Sara Romeo Atance, Juan Viguera Diez, Ola Engkvist, Simon Olsson, Rocío Mercado
Drug Design Using Reinforcement Learning with Graph-Based Deep Generative Models. J. Chem. Inf. Model. 62 (20) (2022)
Jiazhen He, Eva Nittinger, Christian Tyrchan, Werngard Czechtizky, Atanas Patronov, Esben Jannik Bjerrum, Ola Engkvist
Transformer-based molecular optimization beyond matched molecular pairs. J. Cheminformatics 14 (1) (2022)
Iiris Sundin, Alexey Voronov, Haoping Xiao, Kostas Papadopoulos, Esben Jannik Bjerrum, Markus Heinonen, Atanas Patronov, Samuel Kaski, Ola Engkvist
Human-in-the-loop assisted de novo molecular design. J. Cheminformatics 14 (1) (2022)
Hampus Gummesson Svensson, Esben Jannik Bjerrum, Christian Tyrchan, Ola Engkvist, Morteza Haghir Chehreghani
Autonomous Drug Design with Multi-Armed Bandits. Big Data (2022)
Vendy Fialková, Jiaxi Zhao, Kostas Papadopoulos, Ola Engkvist, Esben Jannik Bjerrum, Thierry Kogej, Atanas Patronov
Library Design. J. Chem. Inf. Model. 62 (9) (2022)
Stephen Bonner, Ufuk Kirik, Ola Engkvist, Jian Tang, Ian P. Barrett
Implications of topological imbalance for representation learning on biomedical knowledge graphs. Briefings Bioinform. 23 (5) (2022)
J. Harry Moore, Matthias R. Bauer, Jeff Guo, Atanas Patronov, Ola Engkvist, Christian Margreitter
generated small molecules. Bioinform. 38 (21) (2022)
Stephen Bonner, Ian P. Barrett, Cheng Ye, Rowan Swiers, Ola Engkvist, Andreas Bender, Charles Tapley Hoyt, William L. Hamilton
A review of biomedical datasets relating to drug discovery: a knowledge graph perspective. Briefings Bioinform. 23 (6) (2022)
Stephen Bonner, Ian P. Barrett, Cheng Ye, Rowan Swiers, Ola Engkvist, William L. Hamilton
Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery. CoRR (2021)
Esben Jannik Bjerrum, Amol Thakkar, Ola Engkvist
Artificial applicability labels for improving policies in retrosynthesis prediction. Mach. Learn. Sci. Technol. 2 (1) (2021)
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, James Duffy, Jeremy N. Burrows, J. Mark F. Gardner, Andrew R. Leach
MAIP: a web service for predicting blood-stage malaria inhibitors. J. Cheminformatics 13 (1) (2021)
Maria-Anna Trapotsi, Lewis H. Mervin, Avid M. Afzal, Noé Sturm, Ola Engkvist, Ian P. Barrett, Andreas Bender
Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions. J. Chem. Inf. Model. 61 (3) (2021)
Juan P. Vigueras-Guillén, Arijit Patra, Ola Engkvist, Frank Seeliger
Parallel Capsule Networks for Classification of White Blood Cells. CoRR (2021)
Rocío Mercado, Tobias Rastemo, Edvard Lindelöf, Günter Klambauer, Ola Engkvist, Hongming Chen, Esben Jannik Bjerrum
Graph networks for molecular design. Mach. Learn. Sci. Technol. 2 (2) (2021)
Juan P. Vigueras-Guillén, Arijit Patra, Ola Engkvist, Frank Seeliger
Parallel Capsule Networks for Classification of White Blood Cells. MICCAI (7) (2021)
Stephen Bonner, Ian P. Barrett, Cheng Ye, Rowan Swiers, Ola Engkvist, Andreas Bender, William L. Hamilton
A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective. CoRR (2021)
Samuel Genheden, Ola Engkvist, Esben Jannik Bjerrum
Clustering of Synthetic Routes Using Tree Edit Distance. J. Chem. Inf. Model. 61 (8) (2021)
Jiazhen He, Huifang You, Emil Sandström, Eva Nittinger, Esben Jannik Bjerrum, Christian Tyrchan, Werngard Czechtizky, Ola Engkvist
Molecular optimization by capturing chemist's intuition using deep neural networks. J. Cheminformatics 13 (1) (2021)
Jeff Guo, Jon Paul Janet, Matthias R. Bauer, Eva Nittinger, Kathryn A. Giblin, Kostas Papadopoulos, Alexey Voronov, Atanas Patronov, Ola Engkvist, Christian Margreitter
DockStream: a docking wrapper to enhance de novo molecular design. J. Cheminformatics 13 (1) (2021)
Stephen Bonner, Ufuk Kirik, Ola Engkvist, Jian Tang, Ian P. Barrett
Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs. CoRR (2021)
Lewis H. Mervin, Maria-Anna Trapotsi, Avid M. Afzal, Ian P. Barrett, Andreas Bender, Ola Engkvist
Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty. J. Cheminformatics 13 (1) (2021)
Jie Zhang, Rocío Mercado, Ola Engkvist, Hongming Chen
Comparative Study of Deep Generative Models on Chemical Space Coverage. J. Chem. Inf. Model. 61 (6) (2021)
Igor V. Tetko, Ola Engkvist
From Big Data to Artificial Intelligence: chemoinformatics meets new challenges. J. Cheminformatics 12 (1) (2020)
Josep Arús-Pous, Atanas Patronov, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, Ola Engkvist
SMILES-based deep generative scaffold decorator for de-novo drug design. J. Cheminformatics 12 (1) (2020)
Laurianne David, Amol Thakkar, Rocío Mercado, Ola Engkvist
Molecular representations in AI-driven drug discovery: a review and practical guide. J. Cheminformatics 12 (1) (2020)
Lewis H. Mervin, Avid M. Afzal, Ola Engkvist, Andreas Bender
Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Protein-Ligand Predictions. J. Chem. Inf. Model. 60 (10) (2020)
Michael Withnall, Edvard Lindelöf, Ola Engkvist, Hongming Chen
Building attention and edge message passing neural networks for bioactivity and physical-chemical property prediction. J. Cheminformatics 12 (1) (2020)
Thomas Blaschke, Josep Arús-Pous, Hongming Chen, Christian Margreitter, Christian Tyrchan, Ola Engkvist, Kostas Papadopoulos, Atanas Patronov
REINVENT 2.0: An AI Tool for De Novo Drug Design. J. Chem. Inf. Model. 60 (12) (2020)
Vigneshwari Subramanian, Ekaterina Ratkova, David Palmer, Ola Engkvist, Maxim V. Fedorov, Antonio Llinas
Multisolvent Models for Solvation Free Energy Predictions Using 3D-RISM Hydration Thermodynamic Descriptors. J. Chem. Inf. Model. 60 (6) (2020)
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, Nigel Greene, Tom Vander Aa, Thomas J. Ashby, Sepp Hochreiter, Ola Engkvist, Günter Klambauer, Hongming Chen
Industry-scale application and evaluation of deep learning for drug target prediction. J. Cheminformatics 12 (1) (2020)
Panagiotis-Christos Kotsias, Josep Arús-Pous, Hongming Chen, Ola Engkvist, Christian Tyrchan, Esben Jannik Bjerrum
Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks. Nat. Mach. Intell. 2 (5) (2020)
Samuel Genheden, Amol Thakkar, Veronika Chadimová, Jean-Louis Reymond, Ola Engkvist, Esben Jannik Bjerrum
AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning. J. Cheminformatics 12 (1) (2020)
Noé Sturm, Jiangming Sun, Yves Vandriessche, Andreas Mayr, Günter Klambauer, Lars Carlsson, Ola Engkvist, Hongming Chen
Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models. J. Chem. Inf. Model. 59 (3) (2019)
Michael Withnall, Edvard Lindelöf, Ola Engkvist, Hongming Chen
Attention and Edge Memory Convolution for Bioactivity Prediction. ICANN (Workshop) (2019)
Josep Arús-Pous, Simon Johansson, Oleksii Prykhodko, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, Ola Engkvist
Improving Deep Generative Models with Randomized SMILES. ICANN (Workshop) (2019)
Josep Arús-Pous, Thomas Blaschke, Silas Ulander, Jean-Louis Reymond, Hongming Chen, Ola Engkvist
Exploring the GDB-13 chemical space using deep generative models. J. Cheminformatics 11 (1) (2019)
Oleksii Prykhodko, Simon Johansson, Panagiotis-Christos Kotsias, Josep Arús-Pous, Esben Jannik Bjerrum, Ola Engkvist, Hongming Chen
A de novo molecular generation method using latent vector based generative adversarial network. J. Cheminformatics 11 (1) (2019)
Oliver Laufkötter, Noé Sturm, Jürgen Bajorath, Hongming Chen, Ola Engkvist
Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability. J. Cheminformatics 11 (1) (2019)
Amol Thakkar, Esben Jannik Bjerrum, Ola Engkvist, Jean-Louis Reymond
Neural Network Guided Tree-Search Policies for Synthesis Planning. ICANN (Workshop) (2019)
Josep Arús-Pous, Simon Johansson, Oleksii Prykhodko, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, Ola Engkvist
Randomized SMILES strings improve the quality of molecular generative models. J. Cheminformatics 11 (1) (2019)
Rubén Buendía, Thierry Kogej, Ola Engkvist, Lars Carlsson, Henrik Linusson, Ulf Johansson, Paolo Toccaceli, Ernst Ahlberg
Accurate Hit Estimation for Iterative Screening Using Venn-ABERS Predictors. J. Chem. Inf. Model. 59 (3) (2019)
Lewis H. Mervin, Krishna C. Bulusu, Leen Kalash, Avid M. Afzal, Fredrik Svensson, Mike A. Firth, Ian P. Barrett, Ola Engkvist, Andreas Bender
Orthologue chemical space and its influence on target prediction. Bioinform. 34 (1) (2018)
Rubén Buendía, Ola Engkvist, Lars Carlsson, Thierry Kogej, Ernst Ahlberg
Venn-Abers predictors for improved compound iterative screening in drug discovery. COPA (2018)
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, Hongming Chen
Erratum to: ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics. J. Cheminformatics 9 (1) (2017)
Ernst Ahlberg, Susanne Winiwarter, Henrik Boström, Henrik Linusson, Tuve Löfström, Ulf Norinder, Ulf Johansson, Ola Engkvist, Oscar Hammar, Claus Bendtsen, Lars Carlsson
Using Conformal Prediction to Prioritize Compound Synthesis in Drug Discovery. COPA (2017)
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, Hongming Chen
Molecular De Novo Design through Deep Reinforcement Learning. CoRR (2017)
Jiangming Sun, Lars Carlsson, Ernst Ahlberg, Ulf Norinder, Ola Engkvist, Hongming Chen
Applying Mondrian Cross-Conformal Prediction To Estimate Prediction Confidence on Large Imbalanced Bioactivity Data Sets. J. Chem. Inf. Model. 57 (7) (2017)
Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, Jürgen Bajorath, Hongming Chen
Application of generative autoencoder in de novo molecular design. CoRR (2017)
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, Hongming Chen
ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics. J. Cheminformatics 9 (1) (2017)
Stephanie K. Ashenden, Thierry Kogej, Ola Engkvist, Andreas Bender
Innovation in Small-Molecule-Druggable Chemical Space: Where are the Initial Modulators of New Targets Published? J. Chem. Inf. Model. 57 (11) (2017)
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, Hongming Chen
Molecular de-novo design through deep reinforcement learning. J. Cheminformatics 9 (1) (2017)
Rosa Buonfiglio, Ola Engkvist, Péter L. Várkonyi, Astrid Henz, Elisabet Vikeved, Anders Backlund, Thierry Kogej
Investigating Pharmacological Similarity by Charting Chemical Space. J. Chem. Inf. Model. 55 (11) (2015)
Lewis H. Mervin, Avid M. Afzal, Georgios Drakakis, Richard Lewis, Ola Engkvist, Andreas Bender
Target prediction utilising negative bioactivity data covering large chemical space. J. Cheminformatics 7 (2015)
Jonathan Alvarsson, Martin Eklund, Ola Engkvist, Ola Spjuth, Lars Carlsson, Jarl E. S. Wikberg, Tobias Noeske
Ligand-Based Target Prediction with Signature Fingerprints. J. Chem. Inf. Model. 54 (10) (2014)
Christoph Müller, Daniel L. Ormsby, Isabella Feierberg, Ola Engkvist, Christian Tyrchan, Michael J. Hartshorn
Hit series selection in noisy HTS data: clustering techniques, statistical tests and data visualisations. J. Cheminformatics 6 (S-1) (2014)
Christoph Müller, Isabella Feierberg, Ola Engkvist, Christian Tyrchan
HTS explorer. J. Cheminformatics 6 (S-1) (2014)
Hongming Chen, Yidong Yang, Ola Engkvist
Molecular Topology Analysis of the Differences between Drugs, Clinical Candidate Compounds, and Bioactive Molecules. J. Chem. Inf. Model. 50 (12) (2010)
Andreas Steffen, Thierry Kogej, Christian Tyrchan, Ola Engkvist
Comparison of Molecular Fingerprint Methods on the Basis of Biological Profile Data. J. Chem. Inf. Model. 49 (2) (2009)
Hongming Chen, Ulf Borjesson, Ola Engkvist, Thierry Kogej, Mats Svensson, Niklas Blomberg, Dirk Weigelt, Jeremy N. Burrows, Tim Lange
ProSAR: A New Methodology for Combinatorial Library Design. J. Chem. Inf. Model. 49 (3) (2009)
Thierry Kogej, Ola Engkvist, Niklas Blomberg, Sorel Muresan
Multifingerprint Based Similarity Searches for Targeted Class Compound Selection. J. Chem. Inf. Model. 46 (3) (2006)
Ola Engkvist, Paul Wrede, Ulrich Rester
Prediction of CNS Activity of Compound Libraries Using Substructure Analysis. J. Chem. Inf. Comput. Sci. 43 (1) (2003)
Ola Engkvist, Paul Wrede
High-Throughput, In Silico Prediction of Aqueous Solubility Based on One- and Two-Dimensional Descriptors. J. Chem. Inf. Comput. Sci. 42 (5) (2002)
Jose Manuel Hermida-Ramón, Ola Engkvist, Gunnar Karlström
Theoretical study of intermolecular potential energy surface for HCl dimer: Example of nonspherical atom-atom exchange repulsion interaction. J. Comput. Chem. 19 (16) (1998)
Ola Engkvist, Piotr Borowski, Agneta Bemgård, Gunnar Karlström, Roland Lindh, Anders Colmsjö
On the Relation between Retention Indexes and the Interaction between the Solute and the Column in Gas-Liquid Chromatography. J. Chem. Inf. Comput. Sci. 36 (6) (1996)