Efficient Deep Model Ensemble Framework for Drug-Target Interaction Prediction,The Journal of Physical Chemistry Letters

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Efficient Deep Model Ensemble Framework for Drug-Target Interaction Prediction
The Journal of Physical Chemistry Letters ( IF 4.8 ) Pub Date : 2024-07-22 , DOI: 10.1021/acs.jpclett.4c01509
Jinhang Wei ₁ , Yangbin Zhu ₁ , Linlin Zhuo ₁ , Yang Liu ₂ , Xiangzheng Fu ₃ , Fushan Li _{4,

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Affiliation

Accurate prediction of Drug-Target Interactions (DTI) is crucial for drug development. Current state-of-the-art deep learning methods have significantly advanced the field; however, these methods exhibit limitations in predictive performance and the propensity for false negatives. Therefore, we propose EADTN, a simple and efficient ensemble model. We have designed an innovative feature adaptation technique to automatically extract local weights of drugs and targets, and we utilize clustering-enhanced parameter fine-tuning to overcome the issue of false negatives, thereby enhancing its reliability in drug discovery. Based on EADTN, we also propose a Shapley value-based method for identifying key drug substructures, effectively enhancing the model’s interpretability. Additionally, we utilized EADTN to reveal potential interactions between NQO1 targets and the drugs SIRT-IN-1 and LY2183240, which were subsequently validated through wet-lab experiments. Experimental evidence demonstrates that EADTN consistently outperforms existing best-performing models across various data sets, promising significant benefits in fields such as drug repositioning.

中文翻译：

用于药物-靶标相互作用预测的高效深度模型集成框架

准确预测药物-靶标相互作用（DTI）对于药物开发至关重要。当前最先进的深度学习方法极大地推进了该领域的发展；然而，这些方法在预测性能和假阴性倾向方面存在局限性。因此，我们提出了EADTN，一种简单高效的集成模型。我们设计了一种创新的特征适应技术来自动提取药物和靶标的局部权重，并利用聚类增强的参数微调来克服假阴性问题，从而提高其在药物发现中的可靠性。在EADTN的基础上，我们还提出了一种基于Shapley值的方法来识别关键药物子结构，有效增强了模型的可解释性。此外，我们利用 EADTN 揭示了 NQO1 靶点与药物 SIRT-IN-1 和 LY2183240 之间的潜在相互作用，随后通过湿实验室实验进行了验证。实验证据表明，EADTN 在各种数据集上始终优于现有的表现最佳的模型，有望在药物重新定位等领域带来显着效益。

更新日期：2024-07-22

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