Burden Cas University of Texas descriptors

An alternative to dimension reduction is the use of composite and uncorrelated descriptors that are suitable for the design of information-rich yet low-dimensional chemical spaces. An elegant example is presented by the popular BCUT (Burden-CAS-University of Texas) descriptors (Pearlman and Smith 1998). BCUTs are a set of uncorrelated descriptors that combine information about molecular connectivity, inter-molecular distances, and other molecular properties. BCUT spaces used for many applications are typically only six-dimensional and can frequently be further reduced to 3D representations for visualization purposes by identifying those BCUT axes around which most compounds map. 

D 3D AD ADME ADMET ANN ARD BCI BCUT BNN C4.5 CART ClogP CoMFA CV Two dimensional Three dimensional Applicability domain Absorption, distribution metabolism, and excretion Absorption, distribution metabolism, excretion, and toxicity Artificial neural network Automatic relevance determination Bernard chemical information Burden, CAS, University of Texas descriptors Bayesian neural network Decision trees using information entropy Classification and regression tree Calculated partition coefficient between octanol and water Comparative molecular field analysis Cross-validation... 

Further commonly used molecular descriptors are for example, eigenvalue-based descriptors including 3D information, such as BCUT descriptors (Burden, CAS, University of Texas eigenvalues), EVA, and WHIM descriptors (weighted holistic invariant molecular descriptors). For more information about molecular descriptors, the reader is referred to a handbook by Todeschini and Consonni and a recent review by Xue and Bajorath. ... 

Also commonly used descriptors are some eigenvalue-based descriptors including 3D information, such as BCUT descriptors (Burden, CAS, University of Texas eigen-... 

An alternative to fingerprint based similarities are those based on BCUTs (Burden, CAS, University of Texas). This method uses a modified connectivity matrix (the Burden matrix) onto which are mapped atomic descriptors (such as atomic mass and polarizability) and connectivity information. The eigenvectors of this matrix represent a compressed summary of the information in the matrix and are used to describe a molecule. Typically 5-6 BCUT descriptors suffice to describe the chemical space of a set of molecules, and the space is usually partitioned into distinct bins , with each molecule assigned to the appropriate partition. In this format, similarity calculations become very simple molecules which are mapped into the same partition are similar. As an alternative, one could use larger numbers of molecular properties and a correlation vector approach. 

Burden, CAS, and University of Texas (BCUT) descriptors are well suited and widely used to describe diversity of a chemical population in a low dimensional Euclidian space and they allow for fast cell-based diversity selection algorithms (Pearlman and Smith, 1998). The DiverseSolutions... 

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