Inductive QSAR descriptors. Distinguishing compounds with antibacterial activity by artificial neural networks
Inductive QSAR descriptors. Distinguishing compounds with antibacterial activity by artificial neural networks
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DOI：
10.3390/i6010063
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发表时间：
2005-01-01
影响因子：
5.6
通讯作者：
Cherkasov, A
中科院分区：
文献类型：
--
作者：
Cherkasov, A
在先前的感应效应和空间效应、感应电负性和分子电容模型的基础上，推导了一系列新的感应QSAR描述符。这些分子参数很容易从组成原子的电负性和共价半径以及原子间距离中获得，并且可以反映分子内和分子间相互作用的各个方面。仅使用34个“诱导”QSAR描述符，我们就能够实现93%的具有和不具有抗菌活性的化合物的正确分离（在657个集合中）。基于人工神经网络方法的精心设计的QSAR模型已经得到了广泛的验证，并自信地从文献中为许多试验抗生素分配了抗菌特性。
On the basis of the previous models of inductive and steric effects, 'inductive' electronegativity and molecular capacitance, a range of new 'inductive' QSAR descriptors has been derived. These molecular parameters are easily accessible from electronegativities and covalent radii of the constituent atoms and interatomic distances and can reflect a variety of aspects of intra- and intermolecular interactions. Using 34 'inductive' QSAR descriptors alone we have been able to achieve 93% correct separation of compounds with- and without antibacterial activity ( in the set of 657). The elaborated QSAR model based on the Artificial Neural Networks approach has been extensively validated and has confidently assigned antibacterial character to a number of trial antibiotics from the literature.