NEURAL NETWORK-BASED SEARCH FOR COX-2 ACTIVE LIGANDS FROM COXIB-LIKE AND SIMILAR COMPOUNDS

The development of novel non-steroidal anti-inflammatory drugs (NSAIDs) free of serious side effects remains an attractive area of research. The availability of hundreds of compounds with known inhibitory activity against COX-2, the intended enzyme target of most NSAIDs, provides an excellent opportunity to explore various quantitative structure-activity relationship models and apply them in the binary classification of compounds. In this work, an artificial neural network or neural net (NN) model was constructed on a dataset consisting of 1380 compounds and 184 attributes, i.e., molecular descriptors. A feedforward NN consisting of 63 input nodes, 1 hidden layer with 33 nodes, and trained on 80% of the dataset by a backpropagation algorithm, has learned after 200 training cycles to classify compounds as active or inactive against COX-2. It has excellent predictive performance (accuracy = 93.5%, AUC = 0.97) on the 20% test set. The neural net classified 875 newly designed variants of COX-2 selective inhibitors and 163 structurally related compounds as active against the COX-2 target. The top hits have superior (or at least comparable) binding affinities compared to the control and possess the desirable properties of an oral drug. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, tweak, and build upon the work non commercially, as long as the author is credited and the new creations are licensed under the identical terms.