Department of Mathematics, Faculty of Science, Mugla Sitki Kocman University, 48000 Mugla, Turkey.
Preclinical Department, Faculty of Medicine, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania.
Int J Mol Sci. 2024 Jun 23;25(13):6890. doi: 10.3390/ijms25136890.
This study investigates the clustering patterns of human β-secretase 1 (BACE-1) inhibitors using complex network methodologies based on various distance functions, including Euclidean, Tanimoto, Hamming, and Levenshtein distances. Molecular descriptor vectors such as molecular mass, Merck Molecular Force Field (MMFF) energy, Crippen partition coefficient (ClogP), Crippen molar refractivity (MR), eccentricity, Kappa indices, Synthetic Accessibility Score, Topological Polar Surface Area (TPSA), and 2D/3D autocorrelation entropies are employed to capture the diverse properties of these inhibitors. The Euclidean distance network demonstrates the most reliable clustering results, with strong agreement metrics and minimal information loss, indicating its robustness in capturing essential structural and physicochemical properties. Tanimoto and Hamming distance networks yield valuable clustering outcomes, albeit with moderate performance, while the Levenshtein distance network shows significant discrepancies. The analysis of eigenvector centrality across different networks identifies key inhibitors acting as hubs, which are likely critical in biochemical pathways. Community detection results highlight distinct clustering patterns, with well-defined communities providing insights into the functional and structural groupings of BACE-1 inhibitors. The study also conducts non-parametric tests, revealing significant differences in molecular descriptors, validating the clustering methodology. Despite its limitations, including reliance on specific descriptors and computational complexity, this study offers a comprehensive framework for understanding molecular interactions and guiding therapeutic interventions. Future research could integrate additional descriptors, advanced machine learning techniques, and dynamic network analysis to enhance clustering accuracy and applicability.
本研究采用基于各种距离函数（包括欧几里得、Tanimoto、Hamming 和 Levenshtein 距离）的复杂网络方法研究人β-分泌酶 1（BACE-1）抑制剂的聚类模式。分子描述符向量，如分子量、默克分子力场（MMFF）能量、克里彭分配系数（ClogP）、克里彭摩尔折射度（MR）、偏心度、Kappa 指数、合成可及性评分、拓扑极性表面积（TPSA）和二维/三维自相关熵，用于捕获这些抑制剂的多样性性质。欧几里得距离网络显示出最可靠的聚类结果，具有较强的一致性度量和最小的信息损失，表明其在捕获基本结构和物理化学性质方面的稳健性。Tanimoto 和 Hamming 距离网络产生了有价值的聚类结果，尽管性能中等，而 Levenshtein 距离网络则显示出显著的差异。不同网络中特征向量中心性的分析确定了作为枢纽的关键抑制剂，这些抑制剂可能在生化途径中至关重要。社区检测结果突出了不同的聚类模式，明确的社区提供了 BACE-1 抑制剂的功能和结构分组的见解。该研究还进行了非参数检验，揭示了分子描述符的显著差异，验证了聚类方法。尽管存在局限性，包括对特定描述符和计算复杂性的依赖，但本研究提供了一个全面的框架，用于理解分子相互作用并指导治疗干预。未来的研究可以整合其他描述符、先进的机器学习技术和动态网络分析，以提高聚类准确性和适用性。
