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:作为药物发展的必要过程,找到能够有选择地与特定蛋白结合的药物化合物是极具挑战性和昂贵的。代表着药物目标互动的力度的药物目标亲近性(DTA)在过去十年中在DTI的预测任务中发挥了重要作用。虽然在与DTA有关的研究中应用了深层次的学习,但现有解决办法忽视了药物复合分子分子/蛋白目标分子代表序列学习中的分子分结构之间的基本联系。此外,传统方法缺乏DTA预测过程的可解释性。这导致分子间相互作用缺少特征信息,从而影响预测性能。因此,本文提出了DTA预测方法,其中含有互动学习和自动编码机制的强度。拟议的模型增强了相应的能力,以通过药物/蛋白分子代表学习模块获取单一分子序列的特征信息,补充了分子序列之间在药物复合分子序列/蛋白目标之间的信息互动。DTA价值预测值比DTA的对对互动信息结合了药物目标。此外,本文从理论上证明,DTA预测方法提高了实际的稳定性,从而提高了共同分析结果的稳定性的稳定性。