Nafie Mohamed S, Abu-Elsaoud Abdelghafar M, Diab Mohamed K
Department of Chemistry, College of Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates.
Bioinformatics and Functional Genomics Research Group, Research Institute of Sciences and Engineering (RISE), University of Sharjah, Sharjah 27272, United Arab Emirates.
Comput Struct Biotechnol J. 2025 Jul 13;27:3191-3215. doi: 10.1016/j.csbj.2025.07.016. eCollection 2025.
Computational metabolomics will be established in drug discovery and research on complex biological networks. This field of research enhances the detection of metabolic biomarkers and the prediction of molecular interactions by combining multiscale analysis with and molecular docking methods. These include nuclear magnetic resonance, mass spectrometry, and innovative bioinformatics, which enable the accurate generation and characterization of metabolomes. Molecular docking is a crucial tool for simulating the interaction between ligands and receptors, thereby facilitating the identification of potential therapeutics. It also discusses the potential of metabolomics to inform drug modes of action, from pharmacokinetics to forecasting toxicity, thereby streamlining drug development pipelines. We highlight applications in anticancer, antimicrobial, and antiviral drug discovery and explain how these computational models can accelerate target validation and enhance the accuracy of therapeutic strategies. In addition, this review addresses the current challenges and future directions for computational techniques in conjunction with experimental data to advance personalized medicine. In conclusion, this review aims to highlight the prospective approaches of computational metabolomics and molecular docking that identify evolutionary adaptive metabolisms of multiscale biological systems through their synergistic utilization to overcome the key hurdles involved in both drug discovery and metabolomic research.
计算代谢组学将在药物发现以及复杂生物网络研究中得以确立。该研究领域通过将多尺度分析与分子对接方法相结合，增强了代谢生物标志物的检测以及分子相互作用的预测。这些方法包括核磁共振、质谱分析以及创新型生物信息学，它们能够实现代谢组的精确生成与表征。分子对接是模拟配体与受体之间相互作用的关键工具，从而有助于识别潜在的治疗药物。它还探讨了代谢组学在阐明药物作用模式方面的潜力，从药代动力学到预测毒性，进而简化药物研发流程。我们重点介绍了其在抗癌、抗菌和抗病毒药物发现中的应用，并解释了这些计算模型如何加速靶点验证并提高治疗策略的准确性。此外，本综述探讨了结合实验数据的计算技术在推进个性化医疗方面当前面临的挑战和未来发展方向。总之，本综述旨在突出计算代谢组学和分子对接的前瞻性方法，这些方法通过协同利用来识别多尺度生物系统的进化适应性代谢，以克服药物发现和代谢组学研究中涉及的关键障碍。
