Predicting Complexation Thermodynamic Parameters of β-Cyclodextrin with Chiral Guests by Using Swarm Intelligence and Support Vector Machines

The Particle Swarm Optimization (PSO) and Support Vector Machines (SVMs) approaches are used for predicting the thermodynamic parameters for the 1:1 inclusion complexation of chiral guests with β-cyclodextrin. A PSO is adopted for descriptor selection in the quantitative structure-property relationships (QSPR) of a dataset of 74 chiral guests due to its simplicity, speed, and consistency. The modified PSO is then combined with SVMs for its good approximating properties, to generate a QSPR model with the selected features. Linear, polynomial, and Gaussian radial basis functions are used as kernels in SVMs. All models have demonstrated an impressive performance with R2 higher than 0.8

Hydrogen Bonding: From Small Clusters to Biopolymers

sserstoffbrcken aber auch indirekt die Ausbildung von Biopolymerstrukturen, da sie essentiell an der Struktur des flssigen Wassers mitwirken. Die gegenwrtig zur Verfgung stehenden Daten legen nahe, da die hydrophobe Wechselwirkung zumindest ebenso wichtig ist wenn nicht noch wesentlicher als die direkten Wasserstoffbrcken. Ein neues Konzept zur Beschreibung der Faltung von Proteinen und Nukleinsuren auf der Basis der statistischen Mechanik ermglicht es, die Rolle der Ausbildung von Wasserstoffbrcken auch bei der Nukleation der Faltung und in spteren Phasen des Prozesses zu untersuchen. 1.Introduction The molecular structures of the two most important classes of biopolymers, proteins and nucleic acids, are largely determined by hydrogen bonds, directly since they are important elements of biopolymer structure and indirectly through hydrophobic interactions. Historically, the dominant role of hydrogen bonding became apparent in the early fifties through a few publicati

Holographic Quantitative Structure-Activity Relationships of Tryptamine Derivatives at NMDA, 5HT1A and 5HT2A Receptors

molecule