# A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction

arXiv: https://arxiv.org/abs/2405.14545 · HTML: https://mlsanity.com/paper/2405.14545/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 11 of 11)

- [Bilinear Attention Networks](https://mlsanity.com/paper/1805.07932/) (2018)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction](https://mlsanity.com/paper/2004.11424/) (2020)
- [Multi-Scale Representation Learning on Proteins](https://mlsanity.com/paper/2204.02337/) (2022)
- [DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences](https://mlsanity.com/paper/1811.02114/) (2018)
- [Interpretable bilinear attention network with domain adaptation improves drug-target prediction](https://mlsanity.com/paper/2208.02194/) (2022)
- [PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions](https://mlsanity.com/paper/2008.12249/) (2020)
- [Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction](https://mlsanity.com/paper/1908.06760/) (2019)
- [Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation](https://mlsanity.com/paper/2301.10814/) (2023)
- [FABind: Fast and Accurate Protein-Ligand Binding](https://mlsanity.com/paper/2310.06763/) (2023)
- [Drug-Target Interaction Prediction with Graph Attention networks](https://mlsanity.com/paper/2107.06099/) (2021)

## Built on by (most cited first, 5 of 5)

- [Graph-structured Small Molecule Drug Discovery Through Deep Learning: Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2502.08975/) (2025)
- [FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction](https://mlsanity.com/paper/2406.01651/) (2024)
- [Zero-Shot Learning with Subsequence Reordering Pretraining for Compound-Protein Interaction](https://mlsanity.com/paper/2507.20925/) (2025)
- [Heterogeneous network drug-target interaction prediction model based on graph wavelet transform and multi-level contrastive learning](https://mlsanity.com/paper/2504.20103/) (2025)
- [Structure-Aware Contrastive Learning with Fine-Grained Binding Representations for Drug Discovery](https://mlsanity.com/paper/2509.14788/) (2025)

## Abstract (arXiv)

Drug-target interaction (DTI) prediction is a critical component of the drug discovery process. In the drug development engineering field, predicting novel drug-target interactions is extremely crucial.However, although existing methods have achieved high accuracy levels in predicting known drugs and drug targets, they fail to utilize global protein information during DTI prediction. This leads to an inability to effectively predict interaction the interactions between novel drugs and their targets. As a result, the cross-field information fusion strategy is employed to acquire local and global protein information. Thus, we propose the siamese drug-target interaction SiamDTI prediction method, which utilizes a double channel network structure for cross-field supervised learning.Experimental results on three benchmark datasets demonstrate that SiamDTI achieves higher accuracy levels than other state-of-the-art (SOTA) methods on novel drugs and targets.Additionally, SiamDTI's performance with known drugs and targets is comparable to that of SOTA approachs. The code is available at https://anonymous.4open.science/r/DDDTI-434D.