CODESSA program

Katritzky A R, Jain R, Lomaka A, et al. Correlation of the melting points of potential ionic liquids (imidazolium bromides and benzimi-dazolium bromides) using the codessa program. J. Chem. Inf. Comput. Sci. 2002. 42, 225-231. 

Katritzky, A.R., Petrukhin, R., Perumal, S., Karelson, M., Prakash, 1. and Desai, N. (2002) A QSPR study of sweetness potency using the CODESSA program. Croat. Chem. Acta, 75, 475—502. 

The model of [22] or others described in [17] could be used in the calculation of GCxGC retention times or other parameters (e.g., RI values) for both and columns. However, at the time of writing this chapter, these procedures have not yet been applied in GCxGC. A publication on GCxGC retention time prediction [23] uses the previously mentioned CODESSA program package and experimental retention times obtained from the column in GCxGC, but the data are not included in the calculation. 

The original CODESSA program was realized as an interactive menu system for the MS Windows environment and enabled to generate and use four different types of data sets stmctures, properties, descriptors, and correlations. The program allowed both interactive and file-based input of molecular stmctures and data. The output included textual and numerical data on stmctures, properties and descriptors, and graphical representation of the results of the statistical treatment. 

CODESSA (we tested Version 2.6) stands for comprehensive descriptors for structural and statistical analysis. It is a conventional QSAR/QSPR program. 

Nowadays, more than 4000 types of descriptors are known.17 There exist different ways to classify them. With respect to the type of molecular representation used for their calculations—chemical formula, molecular graph, or spatial positions of atoms—one speaks about ID, 2D, and 3D descriptors, respectively. Descriptors can be global (describing the molecule as a whole) and local (only selected parts are considered). One could distinguish information-based descriptors, which tend to code the information stored in molecular structures, and knowledge-based (or semiempir-ical) descriptors issued from the consideration of the mechanism of action. Most of those descriptors can be obtained with the DRAGON, CODESSA PRO, and ISIDA programs. 

CODESSA PRO Program, http //www.codessa-pro.com/manual/manual.htm, 2008. 

Chemical information can be expressed in a number of ways. Chemical descriptors are commonly used in QSAR. There are thousands of chemical descriptors possibly used in QSAR. Indeed, programs such as DRAGON and CODESSA, just to name a couple, can calculate thousands of parameters [7, 8]. Chemical descriptors have different complexities. Some of them do not require any information on the structure, such as molecular weight. Others refer to the bidimensional structure, such as the number of double bonds. While some others require tridimensional information, such as molecular volume. Chemical descriptors can be geometrical, topological, quantum-mechanical, electrostatical, etc. There are a few books describing chemical descriptors [7, 8],... 

The development of QSAR/QSPR on large descriptor spaces started some time ago. One of the first widely applicable programs for such modeling was CODESSA (Comprehensive DEscriptors for Structural and Statistical Analysis), developed by one of us (M.K.) and Victor Lobanov, a graduate student at that time, in collaboration with Professor Alan R. Katritzky at the University of Florida. In 1994, the first version of the program was published and assessed for the treatment of a variety of chemical and physical properties of compounds and heterocyclic compounds, in particular (1995CSR279, 1997JCICS405). 

CODESSA University of Florida Program automatieally ealculates more than 500 types of descriptors (Constitutional, Topological, Geometrical, Electrostatic, Thermodynamic, Quantum-chemical) http //www.codessa-pro.com/index.htm... 

CODESSA is a fully featured QSAR/QSPR program the primary purpose of which is to tie experimental information to quantum mechanical results, and use the resulting correlations to predict the properties and activities of molecules for which no experimental results exist. To this end, CODESSA can compute over 500 descriptors from an AMPAC results file and correlate these descriptors with provided experimental data using several advanced statistical techniques. 

Semichem s partnership with the University of Florida to produce the QSAi QSPR program CODESSA has further enhanced the usability and general applicability of Semichem s software. CODESSA ties AMPAC results (thermodynamic, electrostatic, geometric, and quantum mechanical) to experimental results, making real-life application easier and more intuitive. Together AMPAC and CODESSA can often predict actual product characteristics and properties. Semichem has also added a PREDICT module, which automates the execution of both AMPAC and CODESSA, with the objective of generating predictions for molecules using an already derived correlation. 

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