Mold2, Molecular Descriptors from 2D Structures for Chemoinformatics and Toxicoinformatics

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• 作者：Huixiao Hong ; Qian Xie ; Weigong Ge ; Feng Qian ; Hong Fang ; Leming Shi ; Zhenqiang Su ; Roger Perkins ; Weida Tong
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2008
• 出版时间：July 2008
• 年：2008
• 卷：48
• 期：7
• 页码：1337-1344
• 全文大小：236K
• 年卷期：v.48,no.7(July 2008)
• ISSN：1549-960X

文摘

Research applications in chemoinformatics and toxicoinformatics increasingly use representations of molecules in the form of numerical descriptors that capture the structural characteristics and properties of molecules. These representations are useful for ADME/toxicity prediction, diversity analysis, library design, QSAR/QSPR, virtual screening, and other purposes. Molecular descriptors have ranged from relatively simple forms calculated from simple two-dimensional (2D) chemical structures to more complex forms representing three-dimensional (3D) chemical structures or complex molecular fingerprints consisting of numerous bit positions to represent specific chemical information. The Mold² software was developed to enable the rapid calculation of a large and diverse set of descriptors encoding two-dimensional chemical structure information. Comparative analysis of Mold² descriptors with those calculated by Cerius², Dragon, and Molconn-Z on several data sets using Shannon entropy analysis demonstrated that Mold² descriptors convey a similar amount of information. In addition, using the same classification method, slightly better models were generated using Mold² descriptors compared to those generated using descriptors from the compared commercial software packages. The low computing cost for Mold² makes it suitable not only for small data sets, such as in QSAR, but also for large databases in virtual screening. High reproducibility and reliability are expected because Mold² does not require 3D structures. Mold² is freely available to the public (http://www.fda.gov/nctr/science/centers/toxicoinformatics/index.htm).