Modality fusion strategies in a transformer-based algorithm predicting enzyme-substrate interactions
G.D. Trevnenski (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Marcel J.T. Reinders – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Jana M. Weber – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
L. Di Fruscia – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
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.