Locating Biologically Active Compounds in Medium-Sized Heterogeneous Datasets by Topological Autocorrelation Vectors:  Dopamine and Benzodiazepine Agonists

Abstract

Electronic properties located on the atoms of a molecule such as partial atomic charges as well as electronegativity and polarizability values are encoded by an autocorrelation vector accounting for the constitution of a molecule. This encoding procedure is able to distinguish between compounds being dopamine agonists and those being benzodiazepine receptor agonists even after projection into a two-dimensional self-organizing network. The two types of compounds can still be distinguished if they are buried in a dataset of 8323 compounds of a chemical supplier catalog comprising a wide structural variety. The maps obtained by this sequence of events, calculation of empirical physicochemical effects, encoding in a topological autocorrelation vector, and projection by a self-organizing neural network, can thus be used for searching for structural similarity, and, in particular, for finding new lead structures with biological activity.

Scaffold‐Hopping Cascade Yields Potent Inhibitors of 5‐Lipoxygenase

“…The first step involved a broad, virtual screening process following the protocol outlined in Figure 1. For each of the 11 queries, two alignment-free similarity searches were performed in the Asinex Gold (November 2005: 231 812 compounds) and Platinum (132 250 compounds) collections (Asinex Ltd., Moscow, Russia) using the "Charge3D" [7,8] and "TripleCharge3D" [7,9] methods. Briefly, "Charge3D" is an implementation of the correlation vector approach developed by Gasteiger and co-workers.…”

“…Briefly, "Charge3D" is an implementation of the correlation vector approach developed by Gasteiger and co-workers. [8] The method compares two molecules based on their three-dimensional distribution of partial atom charges: Euclidian distances of all atom-pair combinations in one molecule are calculated (distances within a certain range are allocated to the same bin), and the charge values of the two atoms that form a pair are multiplied to yield a single value per atom-pair (charge values that were assigned to the same bin were added). Equation (1) describes the autocorrelation vector (CV) calculation used by "Charge3D", where d is the distance in , q i and q j are partial atomic charges, A is the number of atoms in a molecule and d defines the Kronecker delta (1 if a given atom pair exist, 0 otherwise).…”

COMDECOM: Predicting the Lifetime of Screening Compounds in DMSO Solution

“…of topological autocorrelation vectors (AC) 5 using partial charges and atomic polarizabilities as atomic properties. In addition, the topological polar surface area (TPSA) 6 was also included.…”

The neural network MolNet prediction of alkane enthalpies

“…Various numerical representations of organic compounds were proposed in QSPR studies using multi-layer feedforward (MLF) neural models: connection table describing the substituents [6]; modi®ed bond-electron matrix containing as structural information the formal bond order between a pair of atoms and the atomic number Z [7]; molecular graph (topological) distance between methyl groups [8]; constitutional descriptors and topological indices [9]; numerical code [10]; counts of various molecular subgraphs (clusters) [11]; vectorial representation of the chemical structure of the substituents [12]; topostereochemical code describing the environment of an atom [13,14]; the three-dimensional structure encoded in the 3D MORSE (molecule representation of structures based on electron diffraction) representation [15,16]; atom type electrotopological state [17]; presence of a substituent (coded with unity) or absence (coded with zero) [18]; topological autocorrelation vectors [19]; and molecular similarity matrices [20,21].…”