Locating biologically active compounds in medium-sized heterogeneous datasets by topological autocorrelation vectors: dopamine and benzodiazepine agonists.
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.
Bauknecht
H
H
Institut für Parallele und Verteilte Höchstleistungsrechner (IPVR), Universität Stuttgart, Germany. hauknecht@informatik.uni-stuttgart.de
Zell
A
A
Bayer
H
H
Levi
P
P
Wagener
M
M
Sadowski
J
J
Gasteiger
J
J
D016208:Databases, Factual; D018491:Dopamine Agonists / Q000737:chemistry*; D015195:Drug Design; D004563:Electrochemistry; D058785:GABA-A Receptor Agonists; D015394:Molecular Structure; D016571:Neural Networks, Computer
D018491:Dopamine Agonists; D058785:GABA-A Receptor Agonists