Soni Sharmila K, Revathi S Thanga, Sree Pokkuluri Kiran
School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu 603203, India.
Department of Computer Science & Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, Andhra Pradesh 534202, India.
J Bioinform Comput Biol. 2025 Feb;23(1):2550003. doi: 10.1142/S0219720025500039.
Drug-drug interactions (DDIs) pose a major concern in polypharmacy due to their potential to cause unexpected side effects that can adversely affect a patient's health. Therefore, it is crucial to identify DDIs effectively during the early stages of drug discovery and development. In this paper, a novel DDI prediction network (DDINet) is proposed to enhance the predictive performance over conventional DDI methods. Leveraging the DrugBank dataset, drugs are represented using the Simplified Molecular Input Line-Entry System (SMILES), with the RDKit software pre-processing the SMILES strings into their canonical forms. Multiple molecular fingerprinting techniques such as Extended Connectivity Fingerprints (ECFPs), Molecular ACCess System keys (MACCSkeys), PubChem Fingerprints, 3D molecular fingerprints (3D-FP), and molecular dynamics fingerprints (MDFPs) are employed to encode drug chemical structures into feature vectors. Drug similarities are computed using the Tanimoto coefficient (TC), and the final Structural Similarity Profile (SSP) is obtained by averaging the five molecular fingerprint types. The novelty of the approach lies in the integration of a Multi-head Attention centered Weighted Autoencoder (Mul_WAE) as the interaction prediction module, which leverages the Multi-head Attention (MHA) layer to focus on the most significant input features. Furthermore, we introduce the Upgraded Bald Eagle Search Optimization (UBesO) algorithm, which optimally selects the learnable parameters of the Mul_WAE based on cross-entropy loss, improving the model's convergence and performance. The proposed DDINet model achieves an accuracy of 99.77%, 99.66% of AUC, 99.5% average precision, 99.4% precision, and 99.49% recall, providing a comprehensive evaluation of the model's robustness. Beyond high accuracy, DDINet offers advantages in scalability, making it well suited for handling large datasets due to its efficient feature extraction and optimization processes. The unique combination of multiple molecular fingerprinting methods with the MHA layer and UBesO algorithm highlights the innovative aspects of our model and significantly improves prediction performance compared to existing approaches.
药物相互作用（DDIs）在多药治疗中是一个主要问题，因为它们有可能导致意想不到的副作用，对患者健康产生不利影响。因此，在药物发现和开发的早期阶段有效识别药物相互作用至关重要。本文提出了一种新型的药物相互作用预测网络（DDINet），以提高其相对于传统药物相互作用方法的预测性能。利用DrugBank数据集，使用简化分子输入线性输入系统（SMILES）表示药物，通过RDKit软件将SMILES字符串预处理为其标准形式。采用多种分子指纹技术，如扩展连接指纹（ECFPs）、分子访问系统键（MACCSkeys）、PubChem指纹、3D分子指纹（3D-FP）和分子动力学指纹（MDFPs），将药物化学结构编码为特征向量。使用Tanimoto系数（TC）计算药物相似度，并通过对五种分子指纹类型进行平均得到最终的结构相似性概况（SSP）。该方法的新颖之处在于集成了一个以多头注意力为中心的加权自动编码器（Mul_WAE）作为相互作用预测模块，该模块利用多头注意力（MHA）层关注最重要的输入特征。此外，我们引入了升级的秃鹰搜索优化（UBesO）算法，该算法基于交叉熵损失最优地选择Mul_WAE的可学习参数，提高了模型的收敛性和性能。所提出的DDINet模型的准确率达到99.77%，AUC为99.66%，平均精度为99.5%，精度为99.4%，召回率为99.49%，全面评估了模型的稳健性。除了高精度外，DDINet在可扩展性方面具有优势，由于其高效的特征提取和优化过程，非常适合处理大型数据集。多种分子指纹方法与MHA层和UBesO算法的独特组合突出了我们模型的创新之处，与现有方法相比显著提高了预测性能。
