Department of Computer Science: MSc Thesis Presentations

Improvements in Drug-Target Interaction Prediction with Multimodal Deep Learning

Author:  Ilari Tulkki
Advisors: Robert Armah-Sekum and Anchen Li
Supervisor: Juho Rousu

Abstract: Drug-target interaction (DTI) prediction is an important field of computational chemistry with applications in drug discovery and repurposing. This thesis investigates whether integrating predicted 3D structures of drug-target complexes with sequence-based representations improves DTI prediction accuracy. A bimodal deep learning ensemble, BimodalDTI, is introduced. It consists of three components: two graph neural networks operating on complex structures predicted by the Boltz-1 diffusion model, and a sequence-based model that integrates the ChemBERTa and ProtT5 language models. DTI prediction is formulated as a regression task predicting interaction strength. The models are evaluated in bioactivity imputation and new drug scenarios. BimodalDTI consistently outperforms all its individual components and other baseline models. These results indicate that combining predicted structural information with sequence-based representations improves DTI prediction accuracy.