As a necessary process in drug development, finding a drug compound that can selectively bind to a specific protein is highly challenging and costly. Drug-target affinity (DTA), which represents the strength of drug-target interaction (DTI), has played an important role in the DTI prediction task over the past decade. Although deep learning has been applied to DTA-related research, existing solutions ignore fundamental correlations between molecular substructures in molecular representation learning of drug compound molecules/protein targets. Moreover, traditional methods lack the interpretability of the DTA prediction process. This results in missing feature information of intermolecular interactions, thereby affecting prediction performance. Therefore, this paper proposes a DTA prediction method with interactive learning and an autoencoder mechanism. The proposed model enhances the corresponding ability to capture the feature information of a single molecular sequence by the drug/protein molecular representation learning module and supplements the information interaction between molecular sequence pairs by the interactive information learning module. The DTA value prediction module fuses the drug-target pair interaction information to output the predicted value of DTA. Additionally, this paper theoretically proves that the proposed method maximizes evidence lower bound (ELBO) for the joint distribution of the DTA prediction model, which enhances the consistency of the probability distribution between the actual value and the predicted value. The experimental results confirm mutual transformer-drug target affinity (MT-DTA) achieves better performance than other comparative methods.

翻译：作为药物开发中的必要过程，寻找能与特定蛋白质选择性结合的药物化合物极具挑战性且成本高昂。药物-靶点亲和力（DTA）作为药物-靶点相互作用（DTI）强度的表征，在过去十年中在DTI预测任务中发挥了重要作用。尽管深度学习已应用于DTA相关研究，现有解决方案在药物化合物分子/蛋白质靶点的分子表征学习中忽略了分子子结构之间的基本相关性。此外，传统方法缺乏对DTA预测过程的可解释性，这导致分子间相互作用的特征信息缺失，从而影响预测性能。为此，本文提出一种结合交互学习与自编码器机制的DTA预测方法。所提模型通过药物/蛋白质分子表征学习模块增强捕获单一分子序列特征信息的能力，并通过交互信息学习模块补充分子序列对之间的信息交互。DTA值预测模块融合药物-靶点对交互信息，输出DTA预测值。此外，本文从理论上证明该方法最大化了DTA预测模型联合分布的证据下界（ELBO），从而增强了实际值与预测值之间概率分布的一致性。实验结果表明，互变器-药物靶点亲和力（MT-DTA）相较于其他对比方法取得了更优性能。