基于网络的药物再利用用于MYH9相关肾病
Network-based drug repurposing for MYH9-related nephritis
AI总结：
该研究通过网络分析方法，利用多特征框架对MYH9相关的药物库进行分析，揭示了化学空间的非随机组织结构，并提取了共识稳定的化合物用于后续筛选。
摘要：
利用网络理论的工具，我们分析了以MYH9为导向的药物类库在化学空间中的组织结构，采用多特征框架进行分析。数据集来源于ZINC，这是一个公开可用的商业化合物数据库，用于虚拟筛选和药物发现。从6004种分子开始，预处理后得到5000种结构有效且特征完整的化合物。相似性通过Morgan指纹的Tanimoto距离以及单特征距离（xLogP、HBD、HBA、分子量和旋转键）定义。对于每种表示方法，我们构建k近邻网络，并使用Louvain-Leiden算法识别社区。所有网络相对于度数保持的零模型都表现出显著的模块度（Q=0.91-0.99），表明在不同特征下存在明显的非随机化学组织。跨特征的鲁棒性通过覆盖1.25 X 10^7分子对的共聚类矩阵量化，测量化合物对在不同特征特定网络中是否一致地出现在同一社区内。尽管大多数对的协议有限，但出现了一个稀疏的高一致性核心，突显了特征的互补性。从结构和共识相似性导出的最小生成树揭示了不同的骨架拓扑：以骨架驱动的稀疏结构与紧凑的枢纽丰富的共识网络。这些骨架上的介数中心性识别出既在结构上重要又在特征上平衡的化合物。这些结果提供了一种统计上验证的化学空间网络表示，并提供了一种从下游筛选中提取共识稳定化合物的原理化策略。
英文摘要：
Using tools from network theory, we analyze the organization of a MYH9-oriented drug-like library in chemical space using a multi-descriptor framework. The dataset is drawn from ZINC, a publicly available database of commercially accessible compounds curated for virtual screening and drug discovery. Starting from 6004 molecules, preprocessing yields 5000 structurally valid and descriptor-complete compounds. Similarity is defined via Tanimoto distance on Morgan fingerprints and single-descriptor distances for xLogP, HBD, HBA, molecular weight, and rotatable bonds. For each representation, we construct k-nearest-neighbor networks and identify communities using the Louvain-Leiden algorithm. All networks exhibit highly significant modularity (Q=0.91-0.99) relative to degree-preserving null models, demonstrating pronounced nonrandom chemical organization across descriptors. Cross-descriptor robustness is quantified through a co-clustering matrix over 1.25 X 10^7 molecular pairs, measuring how consistently compound pairs co-occur within the same community across descriptor-specific networks. Although most pairs show limited agreement, a sparse high-consensus core emerges, highlighting the complementarity of the descriptors. Minimum spanning trees derived from structural and consensus similarities reveal distinct backbone topologies: a scaffold-driven, sparse structure versus a compact, hub-rich consensus network. Betweenness centrality on these backbones identifies compounds that are both structurally central and descriptor-balanced. These results provide a statistically validated network representation of chemical space and a principled strategy to extract consensus-stable compounds for downstream screening.
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