The identification of drug-target binding affinity (DTA) has attracted
increasing attention in the drug discovery process due to the more specific
interpretation than binary interaction prediction. Recently, numerous deep
learning-based computational methods have been proposed to predict the binding
affinities between drugs and targets benefiting from their satisfactory
performance. However, the previous works mainly focus on encoding biological
features and chemical structures of drugs and targets, with a lack of
exploiting the essential topological information from the drug-target affinity
network. In this paper, we propose a novel hierarchical graph representation
learning model for the drug-target binding affinity prediction, namely
HGRL-DTA. The main contribution of our model is to establish a hierarchical
graph learning architecture to incorporate the intrinsic properties of
drug/target molecules and the topological affinities of drug-target pairs. In
this architecture, we adopt a message broadcasting mechanism to integrate the
hierarchical representations learned from the global-level affinity graph and
the local-level molecular graph. Besides, we design a similarity-based
embedding map to solve the cold start problem of inferring representations for
unseen drugs and targets. Comprehensive experimental results under different
scenarios indicate that HGRL-DTA significantly outperforms the state-of-the-art
models and shows better model generalization among all the scenarios.

提出了一种新的分层图表示学习模型 HGRL-DTA 用于药物靶标亲和力预测，利用全局亲和力网络和局部分子图所学的分层表示，并采用相似性嵌入映射来解决未见药物和靶标的表示问题。实验结果表明，HGRL-DTA 在多种场景下显著优于现有的基准模型，并显示出更好的模型泛化性能。

药物 - 靶标结合亲和力预测的分层图表示学习

Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinity

Accurately predicting drug-target binding affinity (DTA) in silico is a key
task in drug discovery. Most of the conventional DTA prediction methods are
simulation-based, which rely heavily on domain knowledge or the assumption of
having the 3D structure of the targets, which are often difficult to obtain.
Meanwhile, traditional machine learning-based methods apply various features
and descriptors, and simply depend on the similarities between drug-target
pairs. Recently, with the increasing amount of affinity data available and the
success of deep representation learning models on various domains, deep
learning techniques have been applied to DTA prediction. However, these methods
consider either label/one-hot encodings or the topological structure of
molecules, without considering the local chemical context of amino acids and
SMILES sequences. Motivated by this, we propose a novel end-to-end learning
framework, called DeepGS, which uses deep neural networks to extract the local
chemical context from amino acids and SMILES sequences, as well as the
molecular structure from the drugs. To assist the operations on the symbolic
data, we propose to use advanced embedding techniques (i.e., Smi2Vec and
Prot2Vec) to encode the amino acids and SMILES sequences to a distributed
representation. Meanwhile, we suggest a new molecular structure modeling
approach that works well under our framework. We have conducted extensive
experiments to compare our proposed method with state-of-the-art models
including KronRLS, SimBoost, DeepDTA and DeepCPI. Extensive experimental
results demonstrate the superiorities and competitiveness of DeepGS.

本研究介绍了一种使用深度神经网络从氨基酸和 SMILES 序列中提取局部化学上下文并从药物中提取分子结构的端到端学习框架 DeepGS，并使用先进的嵌入技术 Smi2Vec 和 Prot2Vec 对象进行符号数据处理，相比 KronRLS、SimBoost、DeepDTA 和 DeepCPI 等现有模型，实验结果显示出 DeepGS 的优越性和竞争力。