Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China.
Institute of Medical Informatics and Management, Nanjing Medical University, Nanjing, Jiangsu, China.
Bioinformatics. 2022 Oct 31;38(21):4859-4867. doi: 10.1093/bioinformatics/btac597.
The capability to predict the potential drug binding affinity against a protein target has always been a fundamental challenge in silico drug discovery. The traditional experiments in vitro and in vivo are costly and time-consuming which need to search over large compound space. Recent years have witnessed significant success on deep learning-based models for drug-target binding affinity prediction task.
Following the recent success of the Transformer model, we propose a multigranularity protein-ligand interaction (MGPLI) model, which adopts the Transformer encoders to represent the character-level features and fragment-level features, modeling the possible interaction between residues and atoms or their segments. In addition, we use the convolutional neural network to extract higher-level features based on transformer encoder outputs and a highway layer to fuse the protein and drug features. We evaluate MGPLI on different protein-ligand interaction datasets and show the improvement of prediction performance compared to state-of-the-art baselines.
The model scripts are available at https://github.com/IILab-Resource/MGDTA.git.
预测潜在药物与蛋白质靶标的结合亲和力一直是计算机药物发现中的一个基本挑战。传统的体外和体内实验既昂贵又耗时，需要在大量化合物空间中进行搜索。近年来，基于深度学习的药物靶标结合亲和力预测任务的模型取得了显著的成功。
在 Transformer 模型取得最新成功之后，我们提出了一个多粒度蛋白质-配体相互作用（MGPLI）模型，该模型采用 Transformer 编码器来表示字符级特征和片段级特征，模拟残基与原子或其片段之间的可能相互作用。此外，我们使用卷积神经网络基于 transformer 编码器输出提取更高层次的特征，并使用高速公路层融合蛋白质和药物特征。我们在不同的蛋白质-配体相互作用数据集上评估了 MGPLI，并展示了与最先进的基准相比，预测性能的提高。
模型脚本可在 https://github.com/IILab-Resource/MGDTA.git 获得。
