Drug-Target Affinity (DTA) prediction is an essential task for drug discovery
and pharmaceutical research. Accurate predictions of DTA can greatly benefit
the design of new drug. As wet experiments are costly and time consuming, the
supervised data for DTA prediction is extremely limited. This seriously hinders
the application of deep learning based methods, which require a large scale of
supervised data. To address this challenge and improve the DTA prediction
accuracy, we propose a framework with several simple yet effective strategies
in this work: (1) a multi-task training strategy, which takes the DTA
prediction and the masked language modeling (MLM) task on the paired
drug-target dataset; (2) a semi-supervised training method to empower the drug
and target representation learning by leveraging large-scale unpaired molecules
and proteins in training, which differs from previous pre-training and
fine-tuning methods that only utilize molecules or proteins in pre-training;
and (3) a cross-attention module to enhance the interaction between drug and
target representation. Extensive experiments are conducted on three real-world
benchmark datasets: BindingDB, DAVIS and KIBA. The results show that our
framework significantly outperforms existing methods and achieves
state-of-the-art performances, e.g., $0.712$ RMSE on BindingDB IC$_{50}$
measurement with more than $5\%$ improvement than previous best work. In
addition, case studies on specific drug-target binding activities, drug feature
visualizations, and real-world applications demonstrate the great potential of
our work. The code and data are released at this https URL

本文提出了一种基于深度学习的多任务训练、半监督学习和交叉注意力模块的方法，预测药物 - 靶标亲和性，有效地应用于药物设计和制造过程中，实验结果显示此方法具有较高的准确性和广泛的应用前景。

SMT-DTA: 半监督多任务训练提高药物 - 靶标亲和力预测

SMT-DTA: Improving Drug-Target Affinity Prediction with Semi-supervised Multi-task Training

The work for predicting drug and target affinity(DTA) is crucial for drug
development and repurposing. In this work, we propose a novel method called
GDGRU-DTA to predict the binding affinity between drugs and targets, which is
based on GraphDTA, but we consider that protein sequences are long sequences,
so simple CNN cannot capture the context dependencies in protein sequences
well. Therefore, we improve it by interpreting the protein sequences as time
series and extracting their features using Gate Recurrent Unit(GRU) and
Bidirectional Gate Recurrent Unit(BiGRU). For the drug, our processing method
is similar to that of GraphDTA, but uses two different graph convolution
methods. Subsequently, the representation of drugs and proteins are
concatenated for final prediction. We evaluate the proposed model on two
benchmark datasets. Our model outperforms some state-of-the-art deep learning
methods, and the results demonstrate the feasibility and excellent feature
capture ability of our model.

本研究提出一种基于 GraphDTA 的新方法称为 GDGRU-DTA，用于预测药物和靶标之间的结合亲和力，该方法使用 GRU 和 BiGRU 来提取蛋白质序列的特征，并使用两种不同的图卷积方法来处理药物图，最终模型在两个基准数据集上表现出优异的性能。

基于图神经网络和双重门控循环单元的药物靶点结合亲和力预测

GDGRU-DTA: Predicting Drug-Target Binding Affinity Based on GNN and Double GRU

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。