Molecular property prediction has the ability to improve many processes in molecular chemistry industry. One important application is the development of new drugs where molecular property prediction can decrease both the cost and time of finding new drugs. The current trend is to use graph neural networks or transformers which tend to need moderate and large amounts of data respectively to perform well. Because of the scarceness of molecular property data it is of great interest to find an effective method to transfer learning from other more data-abundant problems. In this thesis I present an approach to pre-train transformer encoders on reaction prediction in order to improve performance on downstream molecular property prediction tasks. I have built a model based on the full transformer architecture but modify it for the purpose of pre-training the encoder. Model performance and specifically the effect of pre-training is tested by predicting lipophilicity, HIV inhibition and hERG channel blocking using both pre-trained models and models without any pre-training. The results demonstrate a tendency for improvement of performance on all molecular property prediction tasks using the suggested pre-training but this tendency for improvement is not statistically significant. The major limitation with the conclusive evaluation stems from the limited simulations due to computational constraints

Single-cell genomics (SCG) are methods for investigating heterogeneity between biological cells, among these is Smart-seq which sequences from RNA molecules. A more recent version of this method is Smart-seq3xpress which is currently in the process of being automated by the Sandberg lab at Karolinska Institutet. As part of this automated lab system, microwell plates are moved by a robot arm between molecular biology instuments. The purpose of this project was to create and integrate an image-based classification solution to validate the placement of these plates. This was done by building upon the VGG-16 convolutional neural network (CNN) model and specialising it through transfer learning to train models which classify microwell plate placement as correct or incorrect. These models were then integrated into the automated lab pipeline so that the system could self-correct or warn lab personnel of misplacement, removing the need for constant human supervision. / Enskild cellgenomik (eng. single-cell genomics) är metoder för att undersöka heterogenitet mellan biologiska celler, bland dessa metoder är Smart-seq vilken sekvenserar från RNA molekyler. En nyare version av denna metod är Smart-seq3xpress vilken nu håller på att automatiseras av Sandberglabbet vid Karolinska Institutet. Som del av detta automatiserade labbsystem förflyttas mikrobrunnplattor av en robotarm mellan molekylärbiologiska mätinstrument. Syftet med detta projekt var att skapa samt integrera en bildbaserad klassificeringslösning för att säkerställa placeringen av dessa plattor. Detta gjordes genom att bygga på djupinlärningsmodellen VGG-16 och specialisera den med överförd inlärning för att kunna träna modeller vilka klassificerar om mikrobrunnplattornas placeringar är korrekta eller inkorrekta. Sedan integrerades dessa modeller som en del av det automatiserade labbsystemet sådan att systemet kunde självkorrigera eller varna labbpersonal vid felplaceringar, och därmed ta bort behovet av konstant mänsklig tillsyn.