Drug discovery, especially virtual screening and drug repositioning, can be accelerated through deeper understanding and prediction of Drug Target Interactions (DTIs). The advancement of deep learning as well as the time and financial costs associated with conventional wet-lab experiments have made computational methods for DTI prediction more popular. However, the majority of these computational methods handle the DTI problem as a binary classification task, ignoring the quantitative binding affinity that determines the drug efficacy to their target proteins. Moreover, computational space as well as execution time of the model is often ignored over accuracy. To address these challenges, we introduce a novel method, called Time-efficient Multimodal Drug Target Binding Affinity (TeM-DTBA), which predicts the binding affinity between drugs and targets by fusing different modalities based on compound structures and target sequences. We employ the Lasso feature selection method, which lowers the dimensionality of feature vectors and speeds up the proposed model training time by more than 50%. The results from two benchmark datasets demonstrate that our method outperforms state-of-the-art methods in terms of performance. The mean squared errors of 18.8% and 23.19%, achieved on the KIBA and Davis datasets, respectively, suggest that our method is more accurate in predicting drug-target binding affinity.

通过对药物靶点相互作用（DTI）的更深入理解和预测，可以加速药物发现，特别是虚拟筛选和药物重新定位。深度学习的进步以及与传统湿实验室实验相关的时间和财务成本使得 DTI 预测的计算方法更加流行。然而，这些计算方法中的大多数将 DTI 问题作为二元分类任务来处理，忽略了确定对其靶蛋白的药物功效的定量结合亲和力。此外，模型的计算空间和执行时间常常因准确性而被忽略。为了应对这些挑战，我们引入了一种称为时间高效多模式药物靶点结合亲和力（TeM-DTBA）的新方法，该方法通过基于化合物结构和靶序列融合不同模式来预测药物与靶点之间的结合亲和力。我们采用 Lasso 特征选择方法，降低了特征向量的维数，并将模型训练时间加快了 50% 以上。两个基准数据集的结果表明，我们的方法在性能方面优于最先进的方法。KIBA 和 Davis 数据集上的均方误差分别为 18.8% 和 23.19%，表明我们的方法在预测药物靶点结合亲和力方面更加准确。