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Modeling Discrimination between Antibacterial and Non-Antibacterial Activity based on 3D Molecular Descriptors

机译：基于3D分子描述符的抗菌和非抗菌活性之间的建模区分

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摘要

For a data set of 661 organic chemicals including many drug-like compounds, discrimination between antibacterial and non-antibacterial activity was modeled using hydro-phobicity in terms of the logarithmic octanol/water partition coefficient (log K_(ow)) and AM1-level molecular descriptors encoding geometric, electrostatic, nucleophilic and electrophilic characteristics of the compounds. Linear discriminant analysis (LDA) and binary logistic regression (BLR) achieved an overall classification rate of around 90%, using two to three variables selected from log K_(ow), charged-weighted negative surface area (PNSA-3), positive surface area of heavy atoms (PPSA-1Z), and maximum donor delocalizability (D_(max)~E). Model validation was performed using complementary subsets for training and prediction as well as by training the total set with 50% of the activity data allocated wrongly in several arbitrarily selected ways. The discussion includes a comparative analysis of force-field and AM1 geometries as well as of the 3D variation of AM1-level molecular descriptors. Surprisingly, 3D geometry variations have only little impact on the discriminatory performance of the models.
机译：对于包括许多类药物化合物在内的661种有机化学物质的数据集，使用疏水性根据对数辛醇/水分配系数（log K_（ow））和AM1值对抗菌活性和非抗菌活性进行了区分。编码化合物几何，静电，亲核和亲电特性的分子描述符。线性判别分析（LDA）和二元逻辑回归（BLR）使用从log K_（ow），电荷加权负表面积（PNSA-3），正表面积中选择的两到三个变量，实现了大约90％的总体分类率重原子的面积（PPSA-1Z）和最大供体离域性（D_（max）〜E）。使用补充的子集进行训练和预测，以及通过训练总集，并以几种任意选择的方式错误分配的活动数据的50％进行模型验证。讨论内容包括对力场和AM1几何结构以及AM1级分子描述符的3D变化的比较分析。令人惊讶的是，3D几何变化对模型的区分性能影响很小。

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