论文标题
Deepgs：对药物 - 靶标结合亲和力预测的图形和序列的深度表示
DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction
论文作者
论文摘要
准确地预测硅中的药物靶标结合亲和力（DTA）是药物发现的关键任务。大多数常规的DTA预测方法都是基于仿真的，它们在很大程度上依赖于域知识或具有通常难以获得的目标的3D结构的假设。同时，传统的基于机器学习的方法应用了各种功能和描述符，仅取决于药物目标对之间的相似之处。最近，随着可用的亲和力数据的增加以及对各个领域的深度表示学习模型的成功，深度学习技术已应用于DTA预测。但是，这些方法考虑了标签/一壁编码或分子的拓扑结构，而无需考虑氨基酸和微笑序列的局部化学背景。在此激励的基础上，我们提出了一个新型的端到端学习框架，称为DeepGs，该框架使用深神经网络从氨基酸和微笑序列以及药物中的分子结构中提取局部化学环境。为了协助符号数据的操作，我们建议使用先进的嵌入技术（即SMI2VEC和Prot2Vec）将氨基酸和微笑序列编码为分布式表示。同时，我们建议一种在我们的框架下很好地工作的新分子结构建模方法。我们进行了广泛的实验，将我们提出的方法与包括Kronrls，Simboost，DeepDTA和DeepCPI在内的最新模型进行了比较。广泛的实验结果证明了DEEPG的优势和竞争力。
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.
