Compound classification

Niobium coordination compounds classification and analysis of crystallographic and structural data. C. E. Holloway and M. Melnik, Rev. Inorg. Chem., 1985,7,162 (198). 

Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random forest a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 2003 43(6) 1947-58. 

Table 1. Natural product (NP)-derived drugs launched since 2000 by year with reference to their lead compound, classification, therapeutic area and mechanism of action. 

Bajorath J (2001) Selected concepts and investigations in compound classification, molecular descriptor analysis and virtual screening. J Chem Inf Comput Sci 41 233... 

Three sets of molecular descriptors that can be computed from a molecular connection table are defined. The descriptors are based on the subdivision and classification of the molecular surface area according to atomic properties (such as contribution to logP, molar refractivity, and partial charge). The resulting 32 descriptors are shown (a) to be weakly correlated with each other (b) to encode many traditional molecular descriptors and (c) to be useful for QSAR, QSPAR, and compound classification. 

In the present work, we will use a relatively low level of theory to derive 32 weakly correlated molecular descriptors, each based on the subdivision and classification of the molecular surface area according to three fundamental properties contribution to ClogP, molar refractivity, and atomic partial charge. The resulting collection will be shown to have applicability in QSAR, QSPR, and compound classification. Moreover, the derived 32 descriptors linearly encode most of the information of a collection of traditional mathematical descriptors used in QSAR and QSPR. 

We have also compared MP to cell-based partitioning in a compound classification study focusing on a number of different activity classes. Both partitioning techniques were coupled to a GA and scoring function S2 (see above) was optimized. Results are summarized in Table 2. 

In Chapter 16, Lavine colleagues return to a compound classification problem by using a combination of principal component analysis and a genetic algo-... 

In the 1990s, ECVAM held a forum to vet and evaluate new alternative assays, and developed a list of compounds for testing (24). The key driver for this activity was the fact that DART studies require large numbers of animals. The primary focus of this activity was embryo-fetal toxicity. The list generated from this forum was tested in three assays (later validated by ECVAM) (1) the micromass assay, (2) the rat WEC assay, and (3) the embryonic stem cell test (25). Compounds on the Brown list were classified as either strong, weak, or non-teratogens. The three assays successfully predicted the compound classification about 80% of the time. However, the embryonic stem cell test later performed poorly on a different group of chemicals with known in vivo activities (26). 

The ANN approach has also been applied to compound classification. For example, Drefahl [48] has discussed the ANN approach to discriminate chlorinated organics with respect to their volatility from aqueous solutions. Varmuza [49] has described ANN classification as a standard method in pattern recognition and provides references to its use in spectra interpretation. 

Melnik. M. Heterrurtetallic Coordination Copper Hi Compounds Classification and Analysis of Crystallographic and Structural Guta. Nova Science Publishers. Inc., Huntington. NY. 1999. 

Generally, each chapter covers the physical properties of tour organic chemicals of a similar compound classification For example, the compounds in Chapter 23 are the C lo C aldehydes Chapter 24 corns the Ct to C organic acids For ease of locating the desired organic chemical, the compounds are indexed in the back of the book by formula and name. 

Within each series of the Handbook the entries are ordered using the Beilstein System of Compound Classification this is a set of rules which enables each organic compound to be assigned a position within the Handbook solely on the basis of its molecular structure conversely, knowledge of the rules enables any compound from the c. 1 million compounds already described in the Handbook to be found rapidly, purely on the basis of its structure. A further advantage of the system is that many structurally related compounds are brought together within the same volume of the Handbook. 

Vogt, L., Groger, T. and Zimmermann, R. (2007) Automated compound classification for ambient aerosol sample separations using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry. Journal of Chromatography A, 1150, 2 12. 

This section provides an update on the synthesis, properties, and reactivity of heterophospholes with three or four heteroatoms. The section structure and compound classification are the same as in the original Chapter 4.22 of CHEC-II(1996) <1996CHEC-II(4)771>. 

In addition to cell-based partitioning, statistical partitioning methods are widely used for compound classification. One of the most popular approaches is recursive partitioning (Rusinko et al. 1999), a decision tree method, as illustrated in Figure 1.8. Recursive partitioning divides data sets along decision trees formed by sequences of molecular descriptors. At each node of the tree, a descriptor-based decision is made and the molecular data set is subdivided. For example, a chosen descriptor could simply detect the presence or absence of a structural fragment in a molecule. Alternatively, the... 

Kitchen DB, Stahura FL, Bajorath J (2004) Computational techniques for diversity analysis and compound classification. Mini Rev Med Chem 4 1029-1039. 

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