基于谱图论的物理分子指纹提供高效的化学相似性几何感知度量
Physics-Based Molecular Fingerprints from Spectral Graph Theory Provide Efficient Geometry-Aware Measures of Chemical Similarity
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- Massachusetts Institute of Technology（麻省理工学院）
- University of Stuttgart（斯图加特大学）
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中文总结 AI 辅助
该研究提出基于谱图论的物理分子指纹，编码3D结构且计算高效，可区分同2D连接的不同分子，在多类化学数据集上表现优于基线，适用于化学信息学与机器学习。
中文摘要 AI 辅助
分子表示对于评估分子相似性和构效关系的开发至关重要。尽管已知3D结构对确定化学和物理性质具有重要意义，但最广泛使用的分子指纹仅编码二维连接性，这类表示无法区分相似但不同的立体异构体和构象体。替代的3D方法通常是成对定义的，使其应用于大型化学空间难以实现；而深度学习嵌入具有表现力，但不可解释且受训练数据多样性的限制。在此，我们引入基于谱图论原理的新型受物理启发的分子指纹。我们将分子表示为三维空间中的完全图，边权重编码启发式物理相互作用。对所得图拉普拉斯矩阵进行特征值分解，得到计算高效的固定长度化学指纹，该指纹编码3D结构，同时遵守置换和E(3)不变性的必要物理对称性。谱指纹可区分具有相同2D连接性的独特分子结构，克服了2D描述符的局限性，同时保持了对庞大化学空间进行高效筛选所需的低计算成本。我们使用社区检测算法评估这些指纹，在有机、无机、生物、网状和反应化学的数据集上，观察到其相对于代表性基线的强劲性能。最近邻性质估计和适用域分析揭示了我们的分子表示在机器学习和化学信息学中的实用性。我们预计，谱指纹将成为可泛化、可解释且高效的化学相似性度量，以最低成本纳入3D信息。
英文摘要
Molecular representations are essential for the evaluation of molecular similarity and the development of structure-property relationships. Despite the known importance of 3D structure to determine chemical and physical properties, the most widely used molecular fingerprints encode only two-dimensional connectivity. Such representations fail to distinguish similar but distinct stereoisomers and conformers. Alternative 3D methods are typically defined pairwise, making their application to large chemical spaces prohibitive, while deep learning embeddings are expressive but uninterpretable and limited by their training data diversity. Here, we introduce novel physics-inspired molecular fingerprints based on principles from spectral graph theory. We represent molecules as a complete graph in 3D space, with edge weights encoding heuristic physical interactions. Eigenvalue decomposition of the resulting graph Laplacian matrix results in a computationally efficient fixed-length chemical fingerprint that encodes 3D structure while obeying necessary physical symmetries of permutation and E(3) invariance. Spectral fingerprints differentiate between unique molecular structures with identical 2D connectivity, overcoming a limitation of 2D descriptors, while maintaining the low computational cost needed for efficient screening of vast chemical spaces. We evaluate our fingerprints with community detection algorithms and observe strong performance against representative baselines across datasets from organic, inorganic, biological, reticular, and reaction chemistry. Nearest-neighbor property estimation and applicability domain analyses reveal the utility of our molecular representation in machine learning and cheminformatics. We anticipate that spectral fingerprints will serve as generalizable, interpretable, and efficient measures of chemical similarity that incorporate 3D information at minimal cost.
