Zerong Li

TL;DR: To facilitate more extensive studies of protein and peptides, numerous improvements and updates have been made to PROFEAT, including adding new functions for computing descriptors of protein–protein and protein–small molecule interactions, segment descriptors for local properties of protein sequences, topological descriptor for peptide sequences and small molecule structures.

TL;DR: Support vector machines appears to be potentially useful for facilitating lead discovery in VS of large compound libraries, and the hit-rates and enrichment factors are substantially better than the best results of other VS tools.

TL;DR: This study suggests that currently used descriptor-sets are generally useful for classifying proteins and the prediction performance may be enhanced by exploring combinations of descriptors.

TL;DR: In this article, the reaction mechanisms for thermal decomposition of cyclohexane in the gas phase have been investigated using quantum chemical calculations and transition-state theory, and three series of reaction schemes containing 38 elementary reactions are proposed.

TL;DR: This work explored several statistical learning methods (support vector machines, k-nearest neighbor, probabilistic neural network and C4.5 decision tree) for predicting ER agonists from comprehensive set of known ER agonist and other compounds and suggests that statistical learning techniques such as SVM are potentially useful for facilitating the prediction of ER agonistic and for characterizing the molecular descriptors associated with ER agonism.