Source data of Evidential Deep Learning-based Drug-Target Interaction Prediction

Drug-target interaction (DTI) prediction is a crucial component of drug discovery. Recent deep learning methods show great potential in this field but also encounter substantial challenges. These include generating reliable confidence estimates for predictions, enhancing robustness when handling novel, unseen DTIs, and mitigating the tendency toward overconfident and incorrect predictions. To solve these problems, we propose EviDTI, a novel approach utilizing evidential deep learning (EDL) for uncertainty quantification in neural network-based DTI prediction. EviDTI integrates multiple data dimensions, including drug 2D topological graphs and 3D spatial structures, and target sequence features. Through EDL, EviDTI provides uncertainty estimates for its predictions. Experimental results on three benchmark datasets demonstrate the competitiveness of EviDTI against 11 baseline models. In addition, our study shows that EviDTI can calibrate prediction errors. More importantly, well-calibrated uncertainty information enhances the efficiency of drug discovery by prioritizing DTIs with higher confident predictions for experimental validation. In a case study focused on tyrosine kinase modulators, uncertainty-guided predictions identify novel potential modulators targeting tyrosine kinase FAK and FLT3. These results underscore the potential of evidential deep learning as a robust tool for uncertainty quantification in DTI prediction and its broader implications for accelerating drug discovery.

药物-靶点相互作用（Drug-target Interaction, DTI）预测是药物发现过程中的关键环节。近年来，深度学习方法在该领域展现出巨大应用潜力，但同时也面临诸多严峻挑战：包括为预测结果生成可靠的置信度估计、在处理全新未见过的DTI样本时提升模型鲁棒性，以及缓解模型过度自信且预测错误的倾向。为解决上述问题，本文提出EviDTI——一种基于证据深度学习（Evidential Deep Learning, EDL）的新型方法，用于基于神经网络的DTI预测中的不确定性量化。EviDTI整合了多维度数据模态，包括药物的二维拓扑结构图、三维空间结构，以及靶点序列特征。借助证据深度学习框架，EviDTI可为其预测结果提供不确定性估计值。在三个基准数据集上的实验结果表明，EviDTI的性能优于11种基线模型，具备较强竞争力。此外，本研究证实EviDTI能够对预测误差进行校准。更为关键的是，经过良好校准的不确定性信息可通过优先选择置信度更高的DTI进行实验验证，从而提升药物发现的效率。在一项针对酪氨酸激酶调节剂的案例研究中，基于不确定性引导的预测结果成功识别出了靶向酪氨酸激酶FAK与FLT3的新型潜在调节剂。上述研究结果充分证明，证据深度学习可作为DTI预测中用于不确定性量化的可靠工具，其在加速药物发现领域具备广阔的应用前景。