Issue Date | Title | Author(s) | Source | WOS | Fulltext/Archive link |
2019 | Has Drug Design Augmented by Artificial Intelligence Become a Reality? | Hong-Ming Chen ; Engkvist, O. | Trends in Pharmacological Sciences | | |
2019 | Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies | David, L.; Walsh, J.; Sturm, N.; Feierberg, I.; Nissink, J. W. M.; Hong-Ming Chen ; Bajorath, J.; Engkvist, O. | Chemmedchem | | |
2011 | In silico prediction of unbound brain-to-plasma concentration ratio using machine learning algorithms | Hong-Ming Chen ; Winiwarter, S.; Friden, M.; Antonsson, M.; Engkvist, O. | Journal of Molecular Graphics & Modelling | | |
2020 | Industry-scale application and evaluation of deep learning for drug target prediction | Sturm, N.; Mayr, A.; Van, T. L.; Chupakhin, V.; Ceulemans, H.; Wegner, J.; Golib-Dzib, J. F.; Jeliazkova, N.; Vandriessche, Y.; Bohm, S.; Cima, V.; Martinovic, J.; Greene, N.; Vander Aa, T.; Ashby, T. J.; Hochreiter, S.; Engkvist, O.; Klambauer, G.; Hong-Ming Chen | Journal of Cheminformatics | | |
2013 | Investigation of the influence of molecular topology on ligand binding | Oka, R.; Engkvist, O.; Hong-Ming Chen | Journal of Molecular Graphics & Modelling | | |
2012 | An Investigation of the Relationship Between Molecular Topology and CYP3A4 Inhibition for Drug-like Compounds | Oka, R.; Engkvist, O.; Hong-Ming Chen | Molecular Informatics | | |
2014 | Mining Molecular Pharmacological Effects from Biomedical Text: a Case Study for Eliciting Anti-Obesity/Diabetes Effects of Chemical Compounds | Dura, E.; Muresan, S.; Engkvist, O.; Blomberg, N.; Hong-Ming Chen | Molecular Informatics | | |
2017 | Molecular de-novo design through deep reinforcement learning | Olivecrona, M.; Blaschke, T.; Engkvist, O.; Hong-Ming Chen | Journal of Cheminformatics | | |
2010 | Molecular Topology Analysis of the Differences between Drugs, Clinical Candidate Compounds, and Bioactive Molecules | Hong-Ming Chen ; Yang, Y. D.; Engkvist, O. | Journal of Chemical Information and Modeling | | |
2018 | De novo molecular design using deep reinforcement learning methods | Hong-Ming Chen ; Olivercrona, M.; Blaschke, T.; Engkvist, O.; Kogej, T.; Tyrchan, C. | Abstracts of Papers of the American Chemical Society | | |
2017 | On the Integration of In Silico Drug Design Methods for Drug Repurposing | March-Vila, E.; Pinzi, L.; Sturm, N.; Tinivella, A.; Engkvist, O.; Hong-Ming Chen ; Rastelli, G. | Frontiers in Pharmacology | | |
2014 | On the Relationship between Molecular Hit Rates in High-Throughput Screening and Molecular Descriptors | Hansson, M.; Pemberton, J.; Engkvist, O.; Feierberg, I.; Brive, L.; Jarvis, P.; Zander-Balderud, L.; Hong-Ming Chen | Journal of Biomolecular Screening | | |
2009 | ProSAR: A New Methodology for Combinatorial Library Design | Hong-Ming Chen ; Borjesson, U.; Engkvist, O.; Kogej, T.; Svensson, M. A.; Blomherg, N.; Weigelt, D.; Burrows, J. N.; Lange, T. | Journal of Chemical Information and Modeling | | |
2019 | Randomized SMILES strings improve the quality of molecular generative models | Arus-Pous, J.; Johansson, S. V.; Prykhodko, O.; Bjerrum, E. J.; Tyrchan, C.; Reymond, J. L.; Hong-Ming Chen ; Engkvist, O. | Journal of Cheminformatics | | |
2018 | The rise of deep learning in drug discovery | Hong-Ming Chen ; Engkvist, O.; Wang, Y. H.; Olivecrona, M.; Blaschke, T. | Drug Discovery Today | | |
2020 | SMILES-based deep generative scaffold decorator for de-novo drug design | Arus-Pous, J.; Patronov, A.; Bjerrum, E. J.; Tyrchan, C.; Reymond, J. L.; Hong-Ming Chen ; Engkvist, O. | Journal of Cheminformatics | | |