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/
