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Abstract During the years the National Cancer Institute (NCI) accumulated an enormous amount of information through the application of a complex protocol of drugs screening involving several tumor cell lines, grouped into panels according to the disease class. The Anti‐cancer Agent Mechanism (ACAM) database is a set of 122 compounds with anti‐cancer activity and a reasonably well known mechanism of action, for which are available drug screening data that measure their ability to inhibit growth of a panel of 60 human tumor lines, explicitly designed as a training set for neural network and multivariate analysis. The aim of this work is to adapt a methodology (previously developed for the analysis of DNA minor groove binders) for the analysis of NCI ACAM database, using Principal Component Analysis (PCA) and QSAR/QSPR for the prediction of the mechanism of action of anti‐cancer drugs. The entire database was splitted in a training set of 60 structures and a test set of 48 ones, and each set was expressed in form of a matrix on which further procedures were performed. Three statistical parameters were calculated: First Attempt of Prediction (FAP) expresses the percentage of correct predictions at first attempt, Total Attempt of Prediction (TAP) expresses the total percentage of correct predictions across all the three attempts, Non‐Classified (NC) expresses the percentage of compounds whose mechanism of action has failed to be predicted. The predictive ability of this approach is variable, but the results obtained are generally good; using 50% Growth Inhibiting concentration (GI50) values as training data, we were able to assign a correct mechanism of action with a good degree of reliability (more than 79%).
Lauria et al. (Fri,) studied this question.