FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction
  with Transformer-Driven Interpretation
Ali Khodabandeh Yalabadi Mehdi Yazdani-Jahromi Niloofar Yousefi Aida Tayebi Sina Abdidizaji Ozlem Ozmen Garibay
Drug-Target Interaction (DTI) prediction is vital for drug discovery, yet
challenges persist in achieving model interpretability and optimizing
performance. We propose a novel transformer-based model, FragXsiteDTI, that
aims to address these challenges in DTI prediction. Notably, FragXsiteDTI is
the first DTI model to simultaneously leverage drug molecule fragments and
protein pockets. Our information-rich representations for both proteins and
drugs offer a detailed perspective on their interaction. Inspired by the
Perceiver IO framework, our model features a learnable latent array, initially
interacting with protein binding site embeddings using cross-attention and
later refined through self-attention and used as a query to the drug fragments
in the drug's cross-attention transformer block. This learnable query array
serves as a mediator and enables seamless information translation, preserving
critical nuances in drug-protein interactions. Our computational results on
three benchmarking datasets demonstrate the superior predictive power of our
model over several state-of-the-art models. We also show the interpretability
of our model in terms of the critical components of both target proteins and
drug molecules within drug-target pairs.