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2020 – today
- 2024
[j65]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): 57 (2024)- [j64]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): 95 (2024) - [j63]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): 100 (2024) - [j62]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): 2331-2344 (2024) - [j61]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): 5365-5374 (2024) - [j60]Hampus Gummesson Svensson, Christian Tyrchan, Ola Engkvist, Morteza Haghir Chehreghani:
Utilizing reinforcement learning for de novo drug design. Mach. Learn. 113(7): 4811-4843 (2024) - [j59]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): 25010 (2024) - [j58]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:
A call for an industry-led initiative to critically assess machine learning for real-world drug discovery. Nat. Mac. Intell. 6(10): 1120-1121 (2024) - [c14]Hannah Rosa Friesacher, Emma Svensson, Adam Arany, Lewis H. Mervin, Ola Engkvist:
Temporal Evaluation of Probability Calibration with Experimental Errors. AIDD@ICANN 2024: 13-20 - [c13]Mathias Hilfiker, Leonardo Medrano Sandonas, Marco Klähn, Ola Engkvist, Alexandre Tkatchenko:
Leveraging Quantum Mechanical Properties to Predict Solvent Effects on Large Drug-Like Molecules. AIDD@ICANN 2024: 47-57 - [c12]Yasmine Nahal, Markus Heinonen, Mikhail Kabeshov, Jon Paul Janet, Eva Nittinger, Ola Engkvist, Samuel Kaski:
Towards Interpretable Models of Chemist Preferences for Human-in-the-Loop Assisted Drug Discovery. AIDD@ICANN 2024: 58-70 - [c11]Peter B. R. Hartog, Emma Svensson, Lewis H. Mervin, Samuel Genheden, Ola Engkvist, Igor V. Tetko:
Registries in Machine Learning-Based Drug Discovery: A Shortcut to Code Reuse. AIDD@ICANN 2024: 98-115 - [c10]Emma Svensson, Hannah Rosa Friesacher, Adam Arany, Lewis H. Mervin, Ola Engkvist:
Temporal Evaluation of Uncertainty Quantification Under Distribution Shift. AIDD@ICANN 2024: 132-148 - [i15]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 abs/2402.10064 (2024) - [i14]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 abs/2407.14185 (2024) - [i13]Emma Svensson, Hannah Rosa Friesacher, Susanne Winiwarter, Lewis H. Mervin, Adam Arany, Ola Engkvist:
Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels. CoRR abs/2409.04313 (2024) - [i12]Gökçe Geylan, Jon Paul Janet, Alessandro Tibo, Jiazhen He, Atanas Patronov, Mikhail Kabeshov, Florian David, Werngard Czechtizky, Ola Engkvist, Leonardo De Maria:
PepINVENT: Generative peptide design beyond the natural amino acids. CoRR abs/2409.14040 (2024) - [i11]Hampus Gummesson Svensson, Christian Tyrchan, Ola Engkvist, Morteza Haghir Chehreghani:
Diversity-Aware Reinforcement Learning for de novo Drug Design. CoRR abs/2410.10431 (2024) - [i10]Fabian Krüger, Johan Östman, Lewis H. Mervin, Igor V. Tetko, Ola Engkvist:
Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacy. CoRR abs/2410.16975 (2024) - 2023
- [j57]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): 1099-1113 (2023) - [j56]Samuel Genheden, Per-Ola Norrby, Ola Engkvist:
AiZynthTrain: Robust, Reproducible, and Extensible Pipelines for Training Synthesis Prediction Models. J. Chem. Inf. Model. 63(7): 1841-1846 (2023) - [c9]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: 15576-15584 - [c8]Simon Johansson, Ola Engkvist, Morteza Haghir Chehreghani, Alexander Schliep:
Diverse Data Expansion with Semi-Supervised k-Determinantal Point Processes. IEEE Big Data 2023: 5260-5265 - [i9]Hampus Gummesson Svensson, Christian Tyrchan, Ola Engkvist, Morteza Haghir Chehreghani:
Utilizing Reinforcement Learning for de novo Drug Design. CoRR abs/2303.17615 (2023) - 2022
- [j55]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) - [j54]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) - [j53]J. Harry Moore, Matthias R. Bauer, Jeff Guo, Atanas Patronov, Ola Engkvist, Christian Margreitter:
Icolos: a workflow manager for structure-based post-processing of de novo generated small molecules. Bioinform. 38(21): 4951-4952 (2022) - [j52]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): 18 (2022) - [j51]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): 86 (2022) - [j50]Vendy Fialková, Jiaxi Zhao, Kostas Papadopoulos, Ola Engkvist, Esben Jannik Bjerrum, Thierry Kogej, Atanas Patronov:
LibINVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design. J. Chem. Inf. Model. 62(9): 2046-2063 (2022) - [j49]Rocío Mercado, Esben Jannik Bjerrum, Ola Engkvist:
Exploring Graph Traversal Algorithms in Graph-Based Molecular Generation. J. Chem. Inf. Model. 62(9): 2093-2100 (2022) - [j48]Sara Romeo Atance, Juan Viguera Diez, Ola Engkvist, Simon Olsson, Rocío Mercado:
De Novo Drug Design Using Reinforcement Learning with Graph-Based Deep Generative Models. J. Chem. Inf. Model. 62(20): 4863-4872 (2022) - [j47]Samuel Genheden, Ola Engkvist, Esben Jannik Bjerrum:
Fast prediction of distances between synthetic routes with deep learning. Mach. Learn. Sci. Technol. 3(1): 15018 (2022) - [j46]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): 555-563 (2022) - [j45]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): 731 (2022) - [c7]Hampus Gummesson Svensson, Esben Jannik Bjerrum, Christian Tyrchan, Ola Engkvist, Morteza Haghir Chehreghani:
Autonomous Drug Design with Multi-Armed Bandits. IEEE Big Data 2022: 5584-5592 - [i8]Hampus Gummesson Svensson, Esben Jannik Bjerrum, Christian Tyrchan, Ola Engkvist, Morteza Haghir Chehreghani:
Autonomous Drug Design with Multi-armed Bandits. CoRR abs/2207.01393 (2022) - [i7]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 abs/2210.08871 (2022) - 2021
- [j44]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): 13 (2021) - [j43]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): 26 (2021) - [j42]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): 62 (2021) - [j41]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): 89 (2021) - [j40]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): 1444-1456 (2021) - [j39]Jie Zhang, Rocío Mercado, Ola Engkvist, Hongming Chen:
Comparative Study of Deep Generative Models on Chemical Space Coverage. J. Chem. Inf. Model. 61(6): 2572-2581 (2021) - [j38]Samuel Genheden, Ola Engkvist, Esben Jannik Bjerrum:
Clustering of Synthetic Routes Using Tree Edit Distance. J. Chem. Inf. Model. 61(8): 3899-3907 (2021) - [j37]Esben Jannik Bjerrum, Amol Thakkar, Ola Engkvist:
Artificial applicability labels for improving policies in retrosynthesis prediction. Mach. Learn. Sci. Technol. 2(1): 17001 (2021) - [j36]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): 25023 (2021) - [c6]Juan P. Vigueras-Guillén, Arijit Patra, Ola Engkvist, Frank Seeliger:
Parallel Capsule Networks for Classification of White Blood Cells. MICCAI (7) 2021: 743-752 - [i6]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 abs/2102.10062 (2021) - [i5]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 abs/2105.10488 (2021) - [i4]Juan P. Vigueras-Guillén, Arijit Patra, Ola Engkvist, Frank Seeliger:
Parallel Capsule Networks for Classification of White Blood Cells. CoRR abs/2108.02644 (2021) - [i3]Stephen Bonner, Ufuk Kirik, Ola Engkvist, Jian Tang, Ian P. Barrett:
Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs. CoRR abs/2112.06567 (2021) - 2020
- [j35]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): 1 (2020) - [j34]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): 26 (2020) - [j33]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): 38 (2020) - [j32]Laurianne David, Amol Thakkar, Rocío Mercado, Ola Engkvist:
Molecular representations in AI-driven drug discovery: a review and practical guide. J. Cheminformatics 12(1): 56 (2020) - [j31]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): 70 (2020) - [j30]Igor V. Tetko, Ola Engkvist:
From Big Data to Artificial Intelligence: chemoinformatics meets new challenges. J. Cheminformatics 12(1): 74 (2020) - [j29]Vigneshwari Subramanian, Ekaterina Ratkova, David Palmer, Ola Engkvist, Maxim V. Fedorov, Antonio Llinàs:
Multisolvent Models for Solvation Free Energy Predictions Using 3D-RISM Hydration Thermodynamic Descriptors. J. Chem. Inf. Model. 60(6): 2977-2988 (2020) - [j28]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): 4546-4559 (2020) - [j27]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): 5918-5922 (2020) - [j26]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): 254-265 (2020)
2010 – 2019
- 2019
- [j25]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): 20:1-20:14 (2019) - [j24]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): 54:1-54:14 (2019) - [j23]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): 71:1-71:13 (2019) - [j22]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): 74 (2019) - [j21]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): 962-972 (2019) - [j20]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): 1230-1237 (2019) - [c5]Amol Thakkar, Esben Jannik Bjerrum, Ola Engkvist, Jean-Louis Reymond:
Neural Network Guided Tree-Search Policies for Synthesis Planning. ICANN (Workshop) 2019: 721-724 - [c4]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: 747-751 - [c3]Michael Withnall, Edvard Lindelöf, Ola Engkvist, Hongming Chen:
Attention and Edge Memory Convolution for Bioactivity Prediction. ICANN (Workshop) 2019: 752-757 - 2018
- [j19]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): 72-79 (2018) - [c2]Rubén Buendía, Ola Engkvist, Lars Carlsson, Thierry Kogej, Ernst Ahlberg:
Venn-Abers predictors for improved compound iterative screening in drug discovery. COPA 2018: 201-219 - 2017
- [j18]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): 17:1-17:9 (2017) - [j17]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): 41:1 (2017) - [j16]Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, Hongming Chen:
Molecular de-novo design through deep reinforcement learning. J. Cheminformatics 9(1): 48:1-48:14 (2017) - [j15]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): 1591-1598 (2017) - [j14]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): 2741-2753 (2017) - [c1]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: 174-184 - [i2]Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, Hongming Chen:
Molecular De Novo Design through Deep Reinforcement Learning. CoRR abs/1704.07555 (2017) - [i1]Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, Jürgen Bajorath, Hongming Chen:
Application of generative autoencoder in de novo molecular design. CoRR abs/1711.07839 (2017) - 2015
- [j13]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: 51:1-51:16 (2015) - [j12]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): 2375-2390 (2015) - 2014
- [j11]Christoph Müller, Isabella Feierberg, Ola Engkvist, Christian Tyrchan:
HTS explorer. J. Cheminformatics 6(S-1): 20 (2014) - [j10]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): 27 (2014) - [j9]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): 2647-2653 (2014) - 2010
- [j8]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): 2141-2150 (2010)
2000 – 2009
- 2009
- [j7]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): 338-347 (2009) - [j6]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): 603-614 (2009) - 2006
- [j5]Thierry Kogej, Ola Engkvist, Niklas Blomberg, Sorel Muresan:
Multifingerprint Based Similarity Searches for Targeted Class Compound Selection. J. Chem. Inf. Model. 46(3): 1201-1213 (2006) - 2003
- [j4]Ola Engkvist, Paul Wrede, Ulrich Rester:
Prediction of CNS Activity of Compound Libraries Using Substructure Analysis. J. Chem. Inf. Comput. Sci. 43(1): 155-160 (2003) - 2002
- [j3]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): 1247-1249 (2002)
1990 – 1999
- 1998
- [j2]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): 1816-1825 (1998) - 1996
- [j1]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): 1153-1161 (1996)
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Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
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Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
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Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
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OpenAlex data
Load additional information about publications from 
.
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last updated on 2024-12-10 21:43 CET by the dblp team
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