Abstract
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.
A. Khodabandeh Yalabadi and M. Yazdani-Jahromi—These authors contributed equally.
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Data Availability Statement
All datasets and all instructions and codes for our experiments are publicly available at Github.
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Khodabandeh Yalabadi, A., Yazdani-Jahromi, M., Yousefi, N., Tayebi, A., Abdidizaji, S., Garibay, O.O. (2024). FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretation. In: Ma, J. (eds) Research in Computational Molecular Biology. RECOMB 2024. Lecture Notes in Computer Science, vol 14758. Springer, Cham. https://doi.org/10.1007/978-1-0716-3989-4_5
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