Accurately predicting enzyme-substrate interactions is critical for applications in drug discovery, biocatalysis and protein engineering. Building upon the ProSmith algorithm, a machine learning framework with a multimodal transformer for protein-small molecule interaction prediction, this study introduces protein 3D structural data as an additional modality. To integrate this data, we explore additive and multiplicative modality fusion strategies without requiring retraining the original transformer from scratch. Our experiments demonstrate that while the incorporation of structural data does not offer improved performance in random splits, it has the potential to surpass ProSmith in challenging data splits involving unseen small molecules. Notably, the model shows better generalization for underrepresented substrates.

Video summarization is a task which many researchers have tried to automate with deep learning methods. One of these methods is the SUM-GAN-AAE algorithm developed by Apostolidis et al. which is an unsupervised machine learning method evaluated in this study. The research aims at testing the algorithm's performance on the Breakfast dataset, which is an action localization dataset, and evaluate it with rank correlation coefficients. Parameter optimization was performed to tune the learning rate of the system according to the Breakfast dataset. Then, by using k-fold cross-validation, three metrics were used to evaluate the trained model - F-Score, Kendall's τ and Spearman's ρ. Analysis of the results indicates a high F-Score as reported by the SUM-GAN-AAE paper but low rank correlation coefficients. Moreover, plotting importance scores per frame demonstrates the algorithm's inability to select key frames. The findings suggest that F-Score is not a fitting metric to use in the context of video summarization and the SUM-GAN-AAE algorithm performs poorly not only on action localization datasets but also on video summarization ones such as SumMe.