In this work the use of 2D atom-based quadratic indices is shown in the prediction of proteasome inhibition.
Machine learning approaches such as support vector machine, artificial neural network, random forest and k-nearest
neighbor were used as main techniques to carry out two quantitative structure-activity relationship (QSAR) studies. First,
a database consisting of active and non-active classes was predicted with model performances above 85% and 80% in
learning and test series, respectively. Second a regression-based model was developed which allow to estimate the EC50
with Q2 values of 52.89 and 50.19, in training and prediction sets, respectively, were developed. These results provided
new approaches on proteasome inhibitor identification encouraged by virtual screenings procedures.
Keywords: Atom-based quadratic index, Classification and regression model, Machine learning, Proteasome inhibition,
QSAR, TOMOCOMD-CARDD software.
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