Binary molecular descriptor

Example In molecular structure elucidation the observations are pairs of spectra and compounds. Predictors used are spectral predictors, functions that map spectra onto real numbers. The target variable is, for example, a binary molecular descriptor of a structural property SP, equal to 1 if a compound has property SP, and equal to 0 otherwise. The search is for a function able to predict whether or not the corresponding compound has property SP for a given spectrum. We will calculate such predicting functions in Section 8.5. 

In the first Ccise this is a binary molecular descriptor. The software ToSim written by K. Varmuza [290] uses a vector of binary molecular descriptors to check molecular graphs for similarity. In the following we will use substructure counts (SC) due to their higher information content. 

K. Varmuza and H. Scsibrany. Cluster analysis of chemical structures based on binary molecular descriptors and principal component analysis. Software-Entwicklung in der Chemie, 9 81-90,1995. 

Chemical structures can be described by binary molecular descriptors (used as the Y-matrix in multivariate data analysis). In the case of yes/no-classifications a single binary y-variable can be used to indicate whether a particular structural property is present or not. The type of molecular descriptors (small or large fragments, atom-centered fragments, functional groups or classes of compounds) is essential to obtain a close relationship between structures and spectra. 

Figure 17 Cluster analysis of 44 isomers with molecular formula C6H3CI3 by principal component analysis (score plot of the first and second principal component containing 41.2% and 18.8% of the total variance, respectively). The chemical structures have been characterized by 20 binary molecular descriptors. The common structural properties within each cluster are characterized by the maximum common substructure (MCS). [Reproduced from Ref. 133 with kind permission of Gesellschaft Deutscher Chemiker]...

It is not trivial to define or select substructures that should be considered for this purpose. For the STIRS system, considerable effort has gone into the search for substructures that can be successfully classified by the implemented spectral similarity search. The Mass-Lib system uses a predefined set of 180 binary molecular descriptors to characterize the similarity of structures. In most investigations a more or less arbitrary set of substructures, functional groups or more general structural properties (compound classes) has been considered. Self-adapting methods that automatically analyse the molecular structures in the hitlist (for instance by searching for frequent and large substructures) have not been used up to now in MS. 

The Aspen NRTL-SAC solvent database was identified from the list of solvents presented in the pharmaceutical based International Committee on Harmonization s guidelines for residual solvents in API [28], Hexane, Acetonitrile and Water were selected as the basis for the X, Y and Z segments respectively, the binary interaction parameters for the segments together with molecular descriptors in terms of X,Y and Z segments were then regressed from experimental vapour-liquid and liquid-liquid equilibrium data from the Dechema database. The list of solvent parameters that were used in the case study are given in Table 13. 

To demonstrate the use of binary substructure descriptors and Tanimoto indices for cluster analysis of chemical structures we consider the 20 standard amino acids (Figure 6.3) and characterize each molecular structure by eight binary variables describing presence/absence of eight substructures (Figure 6.4). Note that in most practical applications—for instance, evaluation of results from searches in structure databases—more diverse molecular structures have to be handled and usually several hundred different substructures are considered. Table 6.1 contains the binary substructure descriptors (variables) with value 0 if the substructure is absent and 1 if the substructure is present in the amino acid these numbers form the A-matrix. Binary substructure descriptors have been calculated by the software SubMat (Scsibrany and Varmuza 2004), which requires as input the molecular structures in one file and the substructures in another file, all structures are in Molfile format (Gasteiger and Engel 2003) output is an ASCII file with the binary descriptors. 

The orthogonality of a set of molecular descriptors is a very desirable property. Classification methodologies such as CART (11) (or other decision-tree methods) are not invariant to rotations of the chemistry space. Such methods may encounter difficulties with correlated descriptors (e.g., production of larger decision trees). Often, correlated descriptors necessitate the use of principal components transforms that require a set of reference data for their estimation (at worst, the transforms depend only on the data at hand and, at best, they are trained once from some larger collection of compounds). In probabilistic methodologies, such as Binary QSAR (12), approximation of statistical independence is simplified when uncorrelated descriptors are used. In addition,... 

The first application using MDS in molecular diversity analysis was introduced by a group at Chiron as a means of reducing the enormous dimensionality of binary chemical descriptors They found that 2048-bit Daylight fingerprints associated with 721 commercially available primary amines could be reduced to only five dimensions that reproduced all 260,000 original dissimilarities with a standard deviation of only 10%. Similarly, only seven dimensions were required to reduce the 642,000 pairwise similarities among a set of 1133 carboxylic acids and acid chlorides to the same standard deviation. 

LR Provides a probability of target class membership Models are difficult to interpret Assumes a logistic relationship between target property and molecular descriptors Binary classification only... 

To generate the binary PDR-FP of a compound, its values for the 93 molecular descriptors are calculated, and for each descriptor (represented by n bins), it is determined into which of the predefined n bins the compound descriptor value falls. The associated bit is then set to 1 all other n — 1 bits are set to 0. When this scheme is followed, the bit string representation of any compound has exactly 93 bits that are set on. 

Compounds are described by a number of molecular descriptors these are first normalized and then subjected to the —> Principal Component Analysis to reduce the dimensionality of the chemical space. The M most significant principal components are successively transformed into binary vectors where each bit corresponds to a single principal component (PC) the bit can be either 0 or 1 depending on whether the PC value is smaller or greater than the median of that component calculated on the whole library [Xue, Godden et al., 2003b]. 

Moreover, molecular descriptors different from Free-Wilson descriptors were calculated by transformation of the Free-Wilson matrix through Fourier analysis [Flolik and Halamek, 2002], In this case, Fourier analysis is used to change site- and substituent-oriented binary variables into a few real numbers [Holik and Halamek, 2002]. 

Transformations of a set of molecular descriptors are often performed when there is the need of a —> variable reduction or the need to modify binary vectors, such as site and substituent-oriented variables, into real-valued variable vectors. The milestone of these techniques is the —> Principal Component Analysis (PCA), but also —> Fourier analysis and —> Wavelet analysis are often used, especially for spectra descriptors compression. 

Property filters usually are binary variables assuming a value equal to 1, if the molecule shows a specific property (drug-likeness, ADME properties, and toxicities) and equal to zero otherwise. These filters are not comprised of many molecular descriptors and a threshold or a range of values is associated to each descriptor together with a condition on the descriptor value if the... 

In binary QSAR, the biological activity, expressed in a binary form (1 for active and 0 for inactive) is correlated with molecular descriptors of compounds, and a probability distribution for active and inactive compounds in a training set is estimated. The derived binary QSAR model can subsequently be used to predict the probability of new compounds to be active against a given biological target. 

Slater determinant quantum-chemical descriptors SLOGP lipophilicity descriptors smallest binary label canonical numbering Smallest Set of Smallest Rings ring descriptors SMARTS molecular descriptors... 

Ijjaali, I., Petitet, F., Dubus, E., Barberan, O. and Michel, A. (2007) Assessing potency of c-Jun N-terminal kinase 3 (JNK3) inhibitors using 2D molecular descriptors and binary QSAR methodology. Bioorg. Med. Chem., 15, 4256-4264. 

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