Cycle-Configuration：用于分子推断的新型图论描述符集
Cycle-Configuration: A Novel Graph-theoretic Descriptor Set for Molecular Inference
AI总结：
提出 cycle-configuration（CC）图论描述符以表示芳香环邻/间/对位模式，并将其纳入 mol-infer 的2L模型，在27项化学性质预测上取得相近或更优性能，且可用MILP在实用时间内推断至多50个非氢原子的分子图。
摘要：
本文提出了一族新的化学图描述符，称为 cycle-configuration（CC，环构型），可用于 mol-infer 的标准“两层（2L）模型”；mol-infer 是一个基于混合整数线性规划（MILP）和机器学习（ML）的分子推断框架。所提出的描述符捕捉了芳香环中出现的邻位/间位/对位模式这一概念，而迄今为止该框架尚无法表示这些模式。计算实验表明，当提供这些新描述符时，我们能够针对全部27种受测化学性质构建性能相近或更优的预测函数。我们还给出了一个 MILP 公式，用于在带有 CC 描述符的2L模型（2L+CC模型）下寻求具有期望性质的化学图。结果表明，可在实际可行时间内推断出最多含50个非氢顶点的化学图。
英文摘要：
In this paper, we propose a novel family of descriptors of chemical graphs, named cycle-configuration (CC), that can be used in the standard "two-layered (2L) model" of mol-infer, a molecular inference framework based on mixed integer linear programming (MILP) and machine learning (ML). Proposed descriptors capture the notion of ortho/meta/para patterns that appear in aromatic rings, which has been impossible in the framework so far. Computational experiments show that, when the new descriptors are supplied, we can construct prediction functions of similar or better performance for all of the 27 tested chemical properties. We also provide an MILP formulation that asks for a chemical graph with desired properties under the 2L model with CC descriptors (2L+CC model). We show that a chemical graph with up to 50 non-hydrogen vertices can be inferred in a practical time.
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