Marrero-Ponce Yovani, Medina-Marrero Ricardo, Torrens Francisco, Martinez Yamile, Romero-Zaldivar Vicente, Castro Eduardo A
Department of Pharmacy, Faculty of Chemical-Pharmacy, Central University of Las Villas, Santa Clara 54830, Villa Clara, Cuba.
Bioorg Med Chem. 2005 Apr 15;13(8):2881-99. doi: 10.1016/j.bmc.2005.02.015.
The TOpological MOlecular COMputer Design (TOMOCOMD-CARDD) approach has been introduced for the classification and design of antimicrobial agents using computer-aided molecular design. For this propose, atom, atom-type, and total quadratic indices have been generalized to codify chemical structure information. In this sense, stochastic quadratic indices have been introduced for the description of the molecular structure. These stochastic fingerprints are based on a simple model for the intramolecular movement of all valence-bond electrons. In this work, a complete data set containing 1006 antimicrobial agents is collected and presented. Two structure-based antibacterial activity classification models have been generated. The models (including nonstochastic and stochastic indices) classify correctly more than 90% of 1525 compounds in training sets. These models permit the correct classification of 92.28% and 89.31% of 505 compounds in an external test sets. The TOMOCOMD-CARDD approach, also, satisfactorily compares with respect to nine of the most useful models for antimicrobial selection reported to date. Finally, a virtual screening of 87 new compounds reported in the antiinfective field with antibacterial activities is developed showing the ability of the TOMOCOMD-CARDD models to identify new leads as antibacterial.
拓扑分子计算机设计(TOMOCOMD-CARDD)方法已被引入,用于利用计算机辅助分子设计对抗菌剂进行分类和设计。为此,原子、原子类型和总二次指数已被推广,以编码化学结构信息。从这个意义上说,随机二次指数已被引入用于描述分子结构。这些随机指纹基于一个关于所有价键电子分子内运动的简单模型。在这项工作中,收集并展示了一个包含1006种抗菌剂的完整数据集。生成了两个基于结构的抗菌活性分类模型。这些模型(包括非随机和随机指数)在训练集中对1525种化合物的正确分类率超过90%。在外部测试集中,这些模型对505种化合物的正确分类率分别为92.28%和89.31%。此外,TOMOCOMD-CARDD方法与迄今为止报道的九种最有用的抗菌剂筛选模型相比,效果令人满意。最后,对抗感染领域报道的87种具有抗菌活性的新化合物进行了虚拟筛选,结果表明TOMOCOMD-CARDD模型具有识别新的抗菌先导化合物的能力。