Developing new pharmaceuticals is a costly and time-consuming endeavor fraught with significant safety risks. A critical aspect of drug research and disease therapy is discerning the existence of interactions between drugs and proteins. The evolution of deep learning (DL) in computer science has been remarkably aided in this regard in recent years. Yet, two challenges remain: (i) balancing the extraction of profound, local cohesive characteristics while warding off gradient disappearance and (ii) globally representing and understanding the interactions between the drug and target local attributes, which is vital for delivering molecular level insights indispensable to drug development. In response to these challenges, we propose a DL network structure, MolLoG, primarily comprising two modules: local feature encoders (LFE) and global interactive learning (GIL). Within the LFE module, graph convolution networks and leap blocks capture the local features of drug and protein molecules, respectively. The GIL module enables the efficient amalgamation of feature information, facilitating the global learning of feature structural semantics and procuring multihead attention weights for abstract features stemming from two modalities, providing biologically pertinent explanations for black-box results. Finally, predictive outcomes are achieved by decoding the unified representation via a multilayer perceptron. Our experimental analysis reveals that MolLoG outperforms several cutting-edge baselines across four data sets, delivering superior overall performance and providing satisfactory results when elucidating various facets of drug–target interaction predictions.

中文翻译：

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MolLoG：连接局部与全局的分子水平可解释性模型，用于预测药物靶点相互作用


开发新药物是一项成本高昂且耗时的工作，且充满重大安全风险。药物研究和疾病治疗的一个关键方面是辨别药物和蛋白质之间相互作用的存在。近年来，计算机科学中深度学习（DL）的发展在这方面得到了显着的帮助。然而，仍然存在两个挑战：（i）平衡提取深刻的局部内聚特征，同时避免梯度消失；（ii）全局表示和理解药物和目标局部属性之间的相互作用，这对于提供不可或缺的分子水平见解至关重要到药物开发。为了应对这些挑战，我们提出了一种深度学习网络结构MolLoG，主要包含两个模块：局部特征编码器（LFE）和全局交互式学习（GIL）。在 LFE 模块中，图卷积网络和 Leap 块分别捕获药物和蛋白质分子的局部特征。 GIL 模块能够有效地合并特征信息，促进特征结构语义的全局学习，并为源自两种模态的抽象特征获取多头注意力权重，为黑盒结果提供生物学相关的解释。最后，通过多层感知器解码统一表示来实现预测结果。我们的实验分析表明，MolLoG 在四个数据集上优于多个前沿基线，在阐明药物-靶标相互作用预测的各个方面时提供了卓越的整体性能并提供了令人满意的结果。