Wicker Jerome G P, Cooper Richard I
Chemical Crystallography, University of Oxford , Oxford OX1 3TA, U.K.
J Chem Inf Model. 2016 Dec 27;56(12):2347-2352. doi: 10.1021/acs.jcim.6b00565. Epub 2016 Dec 6.
A new molecular descriptor, nConf, based on chemical connectivity, is presented which captures the accessible conformational space of a molecule. Currently the best available two-dimensional descriptors for quantifying the flexibility of a particular molecule are the rotatable bond count (RBC) and the Kier flexibility index. We present a descriptor which captures this information by sampling the conformational space of a molecule using the RDKit conformer generator. Flexibility has previously been identified as a key feature in determining whether a molecule is likely to crystallize or not. For this application, nConf significantly outperforms previously reported single-variable classifiers and also assists rule-based analysis of black-box machine learning classification algorithms.
本文提出了一种基于化学连通性的新分子描述符nConf，它能够捕捉分子可及的构象空间。目前，用于量化特定分子柔性的最佳二维描述符是可旋转键数（RBC）和基尔柔性指数。我们通过使用RDKit构象生成器对分子的构象空间进行采样，提出了一种能够捕捉此信息的描述符。柔性先前已被确定为决定分子是否可能结晶的关键特征。对于此应用，nConf显著优于先前报道的单变量分类器，并且还有助于基于规则的黑箱机器学习分类算法分析。
