Xianglei Nie

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The ability to identify carcinogenic compounds is of fundamental importance to the safe application of chemicals. In this study, we generated an array of in silico models allowing the classification of compounds into carcinogenic and noncarcinogenic agents based on a data set of 852 noncongeneric chemicals collected from the Carcinogenic Potency Database(More)
The sweetness of a compound is of large interest for the food additive industry. In this work, 2 quantitative models were built to predict the logSw (the logarithm of sweetness) of 320 unique compounds with a molecular weight from 132 to 1287 and a sweetness from 22 to 22500000. The whole dataset was randomly split into a training set including 214(More)
The Aurora kinase family (consisting of Aurora-A, -B and -C) is an important group of enzymes that controls several aspects of cell division in mammalian cells. In this study, 512 compounds of Aurora-A and -B inhibitors were collected. They were classified into three classes: dual Aurora-A and Aurora-B inhibitors, selective inhibitors of Aurora-A and(More)
Using Self-Organizing Map (SOM) and Support Vector Machine (SVM), four classification models were built to predict whether a compound is an active or weakly active inhibitor of Aurora B kinase. A dataset of 679 Aurora B kinase inhibitors was collected, and randomly split into a training set (278 active and 204 weakly active inhibitors) and a test set (109(More)
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