F usion DTI : Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction (2025)

Abstract: Predicting drug-target interaction (DTI) is critical in the drug discovery process. Despite remarkable advances in recent DTI models through the integration of representations from diverse drug and target encoders, such models often struggle to capture the fine-grained interactions between drugs and protein, i.e. the binding of specific drug atoms (or substructures) and key amino acids of proteins, which is crucial for understanding the binding mechanisms and optimising drug design. To address this issue, this paper introduces a novel model, called FusionDTI, which uses a token-level **Fusion** module to effectively learn fine-grained information for **D**rug-**T**arget **I**nteraction. In particular, our FusionDTI model uses the SELFIES representation of drugs to mitigate sequence fragment invalidation and incorporates the structure-aware (SA) vocabulary of target proteins to address the limitation of amino acid sequences in structural information, additionally leveraging pre-trained language models extensively trained on large-scale biomedical datasets as encoders to capture the complex information of drugs and targets. Experiments on three well-known benchmark datasets show that our proposed FusionDTI model achieves the best performance in DTI prediction compared with eight existing state-of-the-art baselines. Furthermore, our case study indicates that FusionDTI could highlight the potential binding sites, enhancing the explainability of the DTI prediction.

## introduction the task of predicting drug-target interactions (dti) plays a pivotal role in the drug discovery progress, as it helps identify potential therapeutic effects of drugs on biological targets facilitating the development of effective treatments @xcite . dti fundamentally relies on the binding * corresponding author of specific drug atoms (or substructures) and key amino acids of proteins @xcite . in particular, each binding site is an interaction between a single amino acid and a single drug atom, which we refer to as a fine-grained interaction. for instance, figure 1 b demonstrates the interaction between hiv-1 protease and the drug lopinavir. a critical component of this interaction is the formation of a hydrogen bond between a ketone group in lopinavir (represented in the selfies @xcite notation as @xcite ) and the side chain of an aspartate residue asp25 (i.e. dd) within the protease @xcite . therefore, capturing such finegrained interaction information during the fusion of drug and target representations is crucial for building effective dti prediction models @xcite @xcite . to obtain representations of drugs and targets for the dti task, some previous studies @xcite have used graph neural networks (gnns) or convolutional neural networks (cnns) using a fixed-size window, potentially leading to a loss of contextual information, especially when drugs and targets are in a longterm sequence. these models directly concatenate the representations together to make predictions without considering fine-grained interactions. more recently, some computational models @xcite employed the fusion module (e.g. deep interactive inference network (diin) @xcite and bilinear attention network (ban) @xcite ) to obtain finegrained interaction information and the 3-mer approach that binds three amino acids together as a target binding site to address the lack of structural information in the amino acid sequence. while useful for highlighting possible regions of interaction, these models do not offer the sufficient granularity needed to gauge the specifics of binding sites, as each binding site only contains one ## conclusions with the rapid increase of new diseases and the urgent need for innovative drugs, it is critical to capture fine-grained interactions, since the binding of specific drug atoms to the main amino acids is key to the dti task. despite some achievements, fine-grained interaction information is not effectively captured. to address this challenge, we introduce fusiondti uses token-level fusion to effectively obtain fine-grained interaction information. through experiments on three well-known datasets, we demonstrate that our proposed fu-siondti model outperforms eight state-of-the-art baselines, particularly in the more realistic crossdomain scenario. additionally, we show that the attention weights of the token-level fusion module can highlight potential binding sites, providing a certain level of explainability.