Screening compounds with image-based analysis is an important part in the processof drug discovery. It is an efficient way to screen compounds as it gives moreinformation than for example HTS. High-content screening as it is also called, hasreally progressed in recent years, as the field of data science evolves, and with it sodoes the efficiency of how images can be processed into information. Anotherimportant part of the drug discovery field is the family of receptors GPCRs, a largefamily of over 800 different receptors in humans. The reason GPCRs are importantin drug discovery is because of the large number of drugs targeting them. In thisexperiment we wanted to use image-based analysis to challenge drugs orcompounds that were said to be specific and see if they actually are that specific, orif we can see indications of the drug also working somewhere else. While the drugswe tested did not appear to cause any morphological perturbations large enough todistinguish them from the control, some drugs appear to cluster differently. Thismight suggest that they affect multiple targets, but it needs to be followed up upon inorder to draw any substantial conclusions.

In recent biomedical research, graph representation learning and cell profiling techniques have emerged as transformative tools for analyzing high-dimensional biological data. The integration of these methods, as investigated in this study, has facilitated an enhanced understanding of complex biological systems, consequently improving drug discovery. The research aimed to decipher connections between chemical structures and cellular phenotypes while incorporating other biological information like proteins and pathways into the workflow. To achieve this, machine learning models' efficacy was examined for classification and regression tasks. The newly proposed graph-level and bio-graph integrative predictors were compared with traditional models. Results demonstrated their potential, particularly in classification tasks. Moreover, the topology of the COVID-19 BioGraph was analyzed, revealing the complex interconnections between chemicals, proteins, and biological pathways. By combining network analysis, graph representation learning, and statistical methods, the study was able to predict active chemical combinations within inactive compounds, thereby exhibiting significant potential for further investigations. Graph-based generative models were also used for molecule generation opening up further research avenues in finding lead compounds. In conclusion, this study underlines the potential of combining graph representation learning and cell profiling techniques in advancing biomedical research in drug repurposing and drug combination. This integration provides a better understanding of complex biological systems, assists in identifying therapeutic targets, and contributes to optimizing molecule generation for drug discovery. Future investigations should optimize these models and validate the drug combination discovery approach. As these techniques continue to evolve, they hold the potential to significantly impact the future of drug screening, drug repurposing, and drug combinations.

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