title: New developments in PEST shape/property hybrid descriptors.
abstract: Recent investigations have shown that the inclusion of hybrid shape/property descriptors together with 2D topological descriptors increases the predictive capability of QSAR and QSPR models. Property-Encoded Surface Translator (PEST) descriptors may be computed using ab initio or semi-empirical electron density surfaces and/or electronic properties, as well as atomic fragment-based TAE/RECON property-encoded surface reconstructions. The RECON and PEST algorithms also include rapid fragment-based wavelet coefficient descriptor (WCD) computation. These descriptors enable a compact encoding of chemical information. We also briefly discuss the use of the RECON/PEST methodology in a virtual high-throughput mode, as well as the use of TAE properties for molecular surface autocorrelation analysis.
mesh_terms: D000465:Algorithms; D006678:HIV / Q000737:chemistry; D008956:Models, Chemical; D021281:Quantitative Structure-Activity Relationship; D012984:Software; D055672:Static Electricity; D014764:Viral Proteins / Q000737:chemistry
chemical_list: D014764:Viral Proteins