Kalakoti Yogesh, Yadav Shashank, Sundar Durai
DAILAB, Department of Biochemical Engineering & Biotechnology, Indian Institute of Technology (IIT) Delhi, New Delhi 110 016, India.
School of Artificial Intelligence, Indian Institute of Technology (IIT) Delhi, New Delhi 110 016, India.
ACS Omega. 2022 Mar 31;7(14):12138-12146. doi: 10.1021/acsomega.2c00424. eCollection 2022 Apr 12.
In silico methods to identify novel drug-target interactions (DTIs) have gained significant importance over conventional techniques owing to their labor-intensive and low-throughput nature. Here, we present a machine learning-based multiclass classification workflow that segregates interactions between active, inactive, and intermediate drug-target pairs. Drug molecules, protein sequences, and molecular descriptors were transformed into machine-interpretable embeddings to extract critical features from standard datasets. Tools such as CHEMBL web resource, iFeature, and an in-house developed deep neural network-assisted drug recommendation (dNNDR)-featx were employed for data retrieval and processing. The models were trained with large-scale DTI datasets, which reported an improvement in performance over baseline methods. External validation results showed that models based on att-biLSTM and gCNN could help predict novel DTIs. When tested with a completely different dataset, the proposed models significantly outperformed competing methods. The validity of novel interactions predicted by dNNDR was backed by experimental and computational evidence in the literature. The proposed methodology could elucidate critical features that govern the relationship between a drug and its target.
由于传统技术劳动强度大且通量低，用于识别新型药物 - 靶点相互作用（DTIs）的计算机方法变得极为重要。在此，我们提出一种基于机器学习的多类分类工作流程，该流程可区分活性、非活性和中间药物 - 靶点对之间的相互作用。药物分子、蛋白质序列和分子描述符被转化为机器可解释的嵌入，以从标准数据集中提取关键特征。诸如CHEMBL网络资源、iFeature以及内部开发的深度神经网络辅助药物推荐（dNNDR） - featx等工具被用于数据检索和处理。这些模型使用大规模DTI数据集进行训练，与基线方法相比性能有所提升。外部验证结果表明，基于注意力双向长短期记忆网络（att - biLSTM）和门控卷积神经网络（gCNN）的模型有助于预测新型DTIs。当用完全不同的数据集进行测试时，所提出的模型显著优于竞争方法。文献中的实验和计算证据支持了dNNDR预测的新型相互作用的有效性。所提出的方法可以阐明控制药物与其靶点之间关系的关键特征。
