Seal S, Trapotsi MA, Spjuth O, Singh S, Carreras-Puigvert J, Greene N, Bender A, Carpenter AE. (2024) Cell Painting: A Decade of Discovery and Innovation in Cellular Imaging. Nature Methods. DOI: 10.1038/s41592-024-02528-8
Seal S, Williams DP, Hosseini-Gerami L, Mahael M, Carpenter AE, Spjuth O, and Bender A. (2024) Improved Early Detection of Drug-Induced Liver Injury by Integrating Predicted in vivo and in vitro Data. Chemical Research in Toxicology. 37, 8, 1290–1305. DOI: 10.1021/acs.chemrestox.4c00015
Ju L, Hellander A, and Spjuth O. (2024) Federated Learning for Predicting Compound Mechanism of Action Based on Image-data from Cell Painting. Artificial Intelligence in Life Sciences. 5, 100098 (2024). DOI: 10.1016/j.ailsci.2024.100098
Carreras-Puigvert J, and Spjuth O. (2024) Artificial Intelligence for High Content Imaging in Drug Discovery. Current Opinion in Structural Biology. 87, 102842. DOI: 10.1016/j.sbi.2024.102842
Seal S, Carreras-Puigvert J, Carpenter AE, Spjuth O, Bender A. (2024) From Pixels to Phenotypes: Integrating Image-Based Profiling with Cell Health Data Improves Interpretability. Molecular Biology of the Cell. 35, 3. DOI: 10.1091/mbc.E23-08-0298
Seal S, Spjuth O, Hosseini-Gerami L, Garcia-Ortegon M, Singh S, Bender A, Carpenter AE. (2024) Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA DICTrank. Journal of Chemical Information and Modeling. 64, 4, 1172-1186. DOI: 10.1021/acs.jcim.3c01834
Harrison PJ, Gupta A, Rietdijk J, Wieslander H, Carreras-Puigvert J, Georgiev P, Wählby C, Spjuth O, Sintorn IM. (2023) Evaluating the utility of brightfield image data for mechanism of action prediction. PLOS Computational Biology. 19, 7, e1011323. DOI: 10.1371/journal.pcbi.1011323
Seal S, Yang H, Trapotsi MA, Singh S, Carreras-Puigvert J, Spjuth O, Bender A. (2023) Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models by Leveraging Similarity to Training Data. Journal of Cheminformatics. 15, 56. DOI: 10.1186/s13321-023-00723-x
Rodríguez MAF, Carreras-Puigvert J, and Spjuth O. (2023) Designing microplate layouts using artificial intelligence. Artificial Intelligence in the Life Sciences. 3, 100073. DOI: 10.1016/j.ailsci.2023.100073
Tian G, Harrison PJ, Sreenivasan AP, Carreras-Puigvert J, Spjuth O. (2023) Combining molecular and cell painting image data for mechanism of action prediction. Artificial Intelligence in Life Science. 3, 100060. DOI: 10.1016/j.ailsci.2023.100060
Olsson H, Kartasalo K, Mulliqi N, Capuccini M, Ruusuvuori P, Samaratunga H, Delahunt B, Lindskog C, Janssen E, Billie A, Egevad L, Spjuth O, and Eklund M. (2022) Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction. Nature Communications. 13, 7761. DOI: 10.1038/s41467-022-34945-8
Sheffield N, Bonazzi V, Bourne P, Burdett T, Clark T, Grossman R, Spjuth O and Yates A. (2022) From biomedical cloud platforms to microservices: next steps in FAIR data and analysis. Nature Scientific Data. 9, 553. DOI: 10.1038/s41597-022-01619-5
Seal S, Carreras-Puigvert J, Trapotsi MA, Yang H, Spjuth O, Bender A. (2022) Integrating Cell Morphology with Gene Expression and Chemical Structure to Aid Mitochondrial Toxicity Detection. Nature Communications Biology. 5, 858. DOI: 10.1038/s42003-022-03763-5
Sreenivasan, A. P., Harrison, P. J., Schaal, W., Matuszewski, D. J., Kultima, K., & Spjuth, O. (2022). Predicting protein network topology clusters from chemical structure using deep learning. Journal of cheminformatics, 14(1), 47. https://doi.org/10.1186/s13321-022-00622-7
Rietdijk, J., Aggarwal, T., Georgieva, P., Lapins, M., Carreras-Puigvert, J., & Spjuth, O. (2022). Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects. The Science of the total environment, 832, 155058. https://doi.org/10.1016/j.scitotenv.2022.155058
Ouyang, W., Bowman, R. W., Wang, H., Bumke, K. E., Collins, J. T., Spjuth, O., Carreras-Puigvert, J., & Diederich, B. (2022). An Open-Source Modular Framework for Automated Pipetting and Imaging Applications. Advanced biology, 6(4), e2101063. https://doi.org/10.1002/adbi.202101063
Rietdijk, J., Tampere, M., Pettke, A., Georgiev, P., Lapins, M., Warpman-Berglund, U., Spjuth, O., Puumalainen, M. R., & Carreras-Puigvert, J. (2021). A phenomics approach for antiviral drug discovery. BMC biology, 19(1), 156. https://doi.org/10.1186/s12915-021-01086-1
Arvidsson McShane, S., Ahlberg, E., Noeske, T., & Spjuth, O. (2021). Machine Learning Strategies When Transitioning between Biological Assays. Journal of chemical information and modeling, 61(7), 3722–3733. https://doi.org/10.1021/acs.jcim.1c00293
Spjuth, O., Frid, J., & Hellander, A. (2021). The machine learning life cycle and the cloud: implications for drug discovery. Expert opinion on drug discovery, 16(9), 1071–1079. https://doi.org/10.1080/17460441.2021.1932812
Harrison, P. J., Wieslander, H., Sabirsh, A., Karlsson, J., Malmsjö, V., Hellander, A., Wählby, C., & Spjuth, O. (2021). Deep-learning models for lipid nanoparticle-based drug delivery. Nanomedicine, 16(13), 1097–1110. https://doi.org/10.2217/nnm-2020-0461
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. https://doi.org/10.1016/j.xphs.2020.09.055
Kensert, A., Harrison, P. J., & Spjuth, O. (2019). Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes. SLAS discovery : advancing life sciences R & D, 24(4), 466–475. https://doi.org/10.1177/2472555218818756
A full list of research publications underlying the Phenaros drug discovery platform is available at https://pharmb.io/publication/
