Ulf Norinder

Articles in journals

• Geylan, G. , De Maria, L. , Engkvist, O. , David, F. & Norinder, U. (2024). A methodology to correctly assess the applicability domain of cell membrane permeability predictors for cyclic peptides.  Digital Discovery. [BibTeX]
• Arvidsson McShane, S. , Norinder, U. , Alvarsson, J. , Ahlberg, E. , Carlsson, L. & Spjuth, O. (2024). CPSign: conformal prediction for cheminformatics modeling.  Journal of Cheminformatics, 16 (1). [BibTeX]
• Béquignon, O. J. M. , Gómez-Tamayo, J. C. , Lenselink, E. B. , Wink, S. , Hiemstra, S. , Lam, C. C. , Gadaleta, D. , Roncaglioni, A. & et al. (2023). Collaborative SAR Modeling and Prospective In Vitro Validation of Oxidative Stress Activation in Human HepG2 Cells.  Journal of Chemical Information and Modeling, 63 (17), 5433-5445. [BibTeX]
• Sapounidou, M. , Norinder, U. & Andersson, P. L. (2023). Predicting Endocrine Disruption Using Conformal Prediction: A Prioritization Strategy to Identify Hazardous Chemicals with Confidence.  Chemical Research in Toxicology, 36 (1), 53-65. [BibTeX]
• Norinder, U. & Lowry, S. (2023). Predicting Larch Casebearer damage with confidence using Yolo network models and conformal prediction.  Remote Sensing Letters, 14 (10), 1023-1035. [BibTeX]
• Tuerkova, A. , Bongers, B. J. , Norinder, U. , Ungvári, O. , Székely, V. , Tarnovskiy, A. , Szakács, G. , Özvegy-Laczka, C. & et al. (2022). Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening.  Journal of Chemical Information and Modeling, 62 (24), 6323-6335. [BibTeX]
• Escher, S. E. , Aguayo-Orozco, A. , Benfenati, E. , Bitsch, A. , Braunbeck, T. , Brotzmann, K. , Bois, F. , van der Burg, B. & et al. (2022). Integrate mechanistic evidence from new approach methodologies (NAMs) into a read-across assessment to characterise trends in shared mode of action.  Toxicology in Vitro, 79. [BibTeX]
• Morger, A. , Garcia de Lomana, M. , Norinder, U. , Svensson, F. , Kirchmair, J. , Mathea, M. & Volkamer, A. (2022). Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data.  Scientific Reports, 12 (1). [BibTeX]
• Morger, A. , Svensson, F. , Arvidsson McShane, S. , Gauraha, N. , Norinder, U. , Spjuth, O. & Volkamer, A. (2021). Assessing the calibration in toxicological in vitro models with conformal prediction.  Journal of Cheminformatics, 13 (1). [BibTeX]
• Garcia de Lomana, M. , Morger, A. , Norinder, U. , Buesen, R. , Landsiedel, R. , Volkamer, A. , Kirchmair, J. & Mathea, M. (2021). ChemBioSim: Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities.  Journal of Chemical Information and Modeling, 61 (7), 3255-3272. [BibTeX]
• Zhang, J. , Norinder, U. & Svensson, F. (2021). Deep Learning-Based Conformal Prediction of Toxicity.  Journal of Chemical Information and Modeling, 61 (6), 2648-2657. [BibTeX]
• Wilm, A. , Garcia de Lomana, M. , Stork, C. , Mathai, N. , Hirte, S. , Norinder, U. , Kühnl, J. & Kirchmair, J. (2021). Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors.  Pharmaceuticals, 14 (8). [BibTeX]
• Alvarsson, J. , Arvidsson McShane, S. , Norinder, U. & Spjuth, O. (2021). Predicting with confidence: Using conformal prediction in drug discovery.  Journal of Pharmaceutical Sciences, 110 (1), 42-49. [BibTeX]
• Wilm, A. , Norinder, U. , Agea, M. I. , de Bruyn Kops, C. , Stork, C. , Kühnl, J. & Kirchmair, J. (2021). Skin Doctor CP: Conformal Prediction of the Skin Sensitization Potential of Small Organic Molecules.  Chemical Research in Toxicology, 34 (2), 330-344. [BibTeX]
• Norinder, U. , Spjuth, O. & Svensson, F. (2021). Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning.  Journal of Cheminformatics, 13 (1). [BibTeX]
• Norinder, U. , Tuck, A. , Norgren, K. & Munic Kos, V. (2020). Existing highly accumulating lysosomotropic drugs with potential for repurposing to target COVID-19.  Biomedicine and Pharmacotherapy, 130. [BibTeX]
• Norinder, U. , Spjuth, O. & Svensson, F. (2020). Using Predicted Bioactivity Profiles to Improve Predictive Modeling.  Journal of Chemical Information and Modeling, 60 (6), 2830-2837. [BibTeX]
• Norinder, U. , Jesús Naveja, J. , Lopez-Lopez, E. , Mucs, D. & Medina-Franco, J. L. (2019). Conformal prediction of HDAC inhibitors.  SAR and QSAR in environmental research (Print), 30 (4), 265-277. [BibTeX]
• Honma, M. , Kitazawa, A. , Cayley, A. , Williams, R. V. , Barber, C. , Hanser, T. , Saiakhov, R. , Chakravarti, S. & et al. (2019). Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project.  Mutagenesis, 34 (1), 3-16. [BibTeX]
• Zhang, J. , Mucs, D. , Norinder, U. & Svensson, F. (2019). LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity–Application to the Tox21 and Mutagenicity Data Sets.  Journal of Chemical Information and Modeling, 59 (10), 4150-4158. [BibTeX]
• Norinder, U. & Svensson, F. (2019). Multitask Modeling with Confidence Using Matrix Factorization and Conformal Prediction.  Journal of Chemical Information and Modeling, 59 (4), 1598-1604. [BibTeX]
• Norinder, U. , Ahlberg, E. & Carlsson, L. (2019). Predicting Ames Mutagenicity Using Conformal Prediction in the Ames/QSAR International Challenge Project.  Mutagenesis, 34 (1), 33-40. [BibTeX]
• Norinder, U. & Munic Kos, V. (2019). QSAR Models for Predicting Five Levels of Cellular Accumulation of Lysosomotropic Macrocycles.  International Journal of Molecular Sciences, 20 (23). [BibTeX]
• Benfenati, E. , Golbamaki, A. , Raitano, G. , Roncaglioni, A. , Manganelli, S. , Lemke, F. , Norinder, U. , Lo Piparo, E. & et al. (2018). A large comparison of integrated SAR/QSAR models of the Ames test for mutagenicity($).  SAR and QSAR in environmental research (Print), 29 (8), 591-611. [BibTeX]
• Jesús Naveja, J. , Norinder, U. , Mucs, D. , López-López, E. & Medina-Franco, J. L. (2018). Chemical space, diversity and activity landscape analysis of estrogen receptor binders.  RSC Advances, 8 (67), 38229-38237. [BibTeX]
• Svensson, F. , Aniceto, N. , Norinder, U. , Cortes-Ciriano, I. , Spjuth, O. , Carlsson, L. & Bender, A. (2018). Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty.  Journal of Chemical Information and Modeling, 58 (5), 1132-1140. [BibTeX]
• Kensert, A. , Alvarsson, J. , Norinder, U. & Spjuth, O. (2018). Evaluating parameters for ligand-based modeling with random forest on sparse data sets.  Journal of Cheminformatics, 10. [BibTeX]
• Lupu, D. , Varshney, M. K. , Mucs, D. , Inzunza, J. , Norinder, U. , Loghin, F. , Nalvarte, I. & Ruegg, J. (2018). Fluoxetine Affects Differentiation of Midbrain Dopaminergic Neurons In Vitro.  Molecular Pharmacology, 94 (4), 1220-1231. [BibTeX]
• Svensson, F. , Afzal, A. M. , Norinder, U. & Bender, A. (2018). Maximizing gain in high-throughput screening using conformal prediction.  Journal of Cheminformatics, 10 (1). [BibTeX]
• Norinder, U. , Myatt, G. & Ahlberg, E. (2018). Predicting Aromatic Amine Mutagenicity With Confidence: A Case Study Using Conformal Prediction.  Biomolecules, 8 (3). [BibTeX]
• Forreryd, A. , Norinder, U. , Lindberg, T. & Lindstedt, M. (2018). Predicting skin sensitizers with confidence: Using conformal prediction to determine applicability domain of GARD.  Toxicology in Vitro, 48, 179-187. [BibTeX]
• Ljunggren, S. A. , Helmfrid, I. , Norinder, U. , Fredriksson, M. , Wingren, G. , Karlsson, H. & Lindahl, M. (2017). Alterations in high-density lipoprotein proteome and function associated with persistent organic pollutants.  Environment International, 98, 204-211. [BibTeX]
• Svensson, F. , Norinder, U. & Bender, A. (2017). Improving Screening Efficiency through Iterative Screening Using Docking and Conformal Prediction.  Journal of Chemical Information and Modeling, 57 (3), 439-444. [BibTeX]
• Svensson, F. , Norinder, U. & Bender, A. (2017). Modelling compound cytotoxicity using conformal prediction and PubChem HTS data.  Toxicology Research, 6 (1), 73-80. [BibTeX]
• Lindh, M. , Karlen, A. & Norinder, U. (2017). Predicting the Rate of Skin Penetration Using an Aggregated Conformal Prediction Framework.  Molecular Pharmaceutics, 14 (5), 1571-1576. [BibTeX]
• Attoff, K. , Gliga, A. , Lundqvist, J. , Norinder, U. & Forsby, A. (2017). Whole genome microarray analysis of neural progenitor C17.2 cells during differentiation and validation of 30 neural mRNA biomarkers for estimation of developmental neurotoxicity.  PLOS ONE, 12 (12). [BibTeX]
• Vandenberg, L. N. , Agerstrand, M. , Beronius, A. , Beausoleil, C. , Bergman, Å. , Bero, L. A. , Bornehag, C. , Boyer, C. S. & et al. (2016). A proposed framework for the systematic review and integrated assessment (SYRINA) of endocrine disrupting chemicals.  Environmental Health, 15 (1). [BibTeX]
• Norinder, U. , Rybacka, A. & Andersson, P. L. (2016). Conformal prediction to define applicability domain: A case study on predicting ER and AR binding.  SAR and QSAR in environmental research (Print), 27 (4), 303-316. [BibTeX]
• Over, B. , Matsson, P. , Tyrchan, C. , Artursson, P. , Doak, B. C. , Foley, M. A. , Hilgendorf, C. , Johnston, S. E. & et al. (2016). Structural and conformational determinants of macrocycle cell permeability.  Nature Chemical Biology, 12 (12), 1065-1074. [BibTeX]
• Eklund, M. , Norinder, U. , Boyer, S. & Carlsson, L. (2015). The application of conformal prediction to the drug discovery process.  Annals of Mathematics and Artificial Intelligence, 74 (1-2), 117-132. [BibTeX]
• Eklund, M. , Norinder, U. , Boyer, S. & Carlsson, L. (2014). Choosing Feature Selection and Learning Algorithms in QSAR.  Journal of Chemical Information and Modeling, 54 (3), 837-843. [BibTeX]
• Karunaratne, T. , Boström, H. & Norinder, U. (2013). Comparative analysis of the use of chemoinformatics-based and substructure-based descriptors for quantitative structure-activity relationship (QSAR) modeling.  Intelligent Data Analysis, 17 (2), 327-341. [BibTeX]
• Norinder, U. & Boström, H. (2013). Representing descriptors derived from multiple conformations as uncertain features for machine learning.  Journal of Molecular Modeling, 19 (6), 2679-2685. [BibTeX]

Conference papers

• Alijagic, A. , Scherbak, N. , Kotlyar, O. , Karlsson, P. , Persson, A. , Hedbrant, A. , Norinder, U. , Larsson, M. & et al. (2022). Cell Painting unveils cell response signatures to (nano)particles formed in additive manufacturing. In:  Toxicology Letters. Paper presented at XVIth International Congress of Toxicology (ICT 2022) - UNITING IN TOXICOLOGY, Maastricht, The Netherlands, September 18-21, 2022. (pp. S226-S227). Elsevier. [BibTeX]
• Sapounidou, M. , Norinder, U. & Andersson, P. L. (2021). Application of conformal prediction for in silico definition of molecular initiating events linked to endocrine disruption. In:  Toxicology Letters. Paper presented at 56th Congress of the European Societies of Toxicology (EUROTOX 2021), Virtual Congress, September 27 – October 1, 2021. (pp. S86-S86). Elsevier. [BibTeX]
• Linusson, H. , Norinder, U. , Boström, H. , Johansson, U. & Löfström, T. (2017). On the Calibration of Aggregated Conformal Predictors. In:  Proceedings of Machine Learning Research. Paper presented at Conformal and Probabilistic Prediction and Applications, Stockholm Sweden 13-16 June, 2017. [BibTeX]
• Ahlberg, E. , Winiwarter, S. , Boström, H. , Linusson, H. , Löfström, T. , Norinder, U. , Johansson, U. , Engkvist, O. & et al. (2017). Using conformal prediction to prioritize compound synthesis in drug discovery. In: Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, and Harris Papadopoulos,  Proceedings of Machine Learning Research Volume 60: Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden. Paper presented at The 6th Symposium on Conformal and Probabilistic Prediction with Applications, (COPA 2017), 13-16 June, 2017, Stockholm, Sweden. (pp. 174-184). Stockholm: [BibTeX]
• Capuccini, M. , Carlsson, L. , Norinder, U. & Spjuth, O. (2015). Conformal prediction in Spark: Large-scale machine learning with confidence. In: Raicu, I.; Rana, O.; Buyya, R.,  Proc. 2nd International Symposium on Big Data Computing. Paper presented at International Symposium on Big Data Computing, December 7–10, Limassol, Cyprus, 2015. (pp. 61-67). IEEE Computer Society. [BibTeX]