Deep learning analysis of intracranial EEG for recognizing drug effects and mechanisms of action

Predicting novel drug properties is fundamental to polypharmacology, repositioning, and the study of biologically active substances during the preclinical phase. The use of machine learning, including deep learning methods, for the identification of drug – target interactions has gained increasing popularity in recent years.
The objective of this study was to develop a method for recognizing psychotropic effects and drug mechanisms of action (drug – target interactions) based on an analysis of the bioelectrical activity of the brain using artificial intelligence technologies.
Intracranial electroencephalographic (EEG) signals from rats were recorded (4 channels at a sampling frequency of 500 Hz) after the administration of psychotropic drugs (gabapentin, diazepam, carbamazepine, pregabalin, eslicarbazepine, phenazepam, arecoline, pentylenetetrazole, picrotoxin, pilocarpine, chloral hydrate). The signals were divided into 2-second epochs, then converted into 2000$\times$4 images and input into an autoencoder. The output of the bottleneck layer was subjected to classification and clustering using t-SNE, and then the distances between resulting clusters were calculated. As an alternative, an approach based on feature extraction with dimensionality reduction using principal component analysis and kernel support vector machine (kSVM) classification was used. Models were validated using 5-fold cross-validation.
The classification accuracy obtained for 11 drugs during cross-validation was 0.580$\pm$0.021, which is significantly higher than the accuracy of the random classifier (0.091$\pm$0.045, p<0.0001) and the kSVM (0.441$\pm$0.035, p<0.05). t-SNE maps were generated from the bottleneck parameters of intracranial EEG signals. The relative proximity of the signal clusters in the parametric space was assessed.
The present study introduces an original method for biopotential-mediated prediction of effects and mechanism of action (drug – target interaction). This method employs convolutional neural networks in conjunction with a modified selective parameter reduction algorithm. Post-treatment EEGs were compressed into a unified parameter space. Using a neural network classifier and clustering, we were able to recognize the patterns of neuronal response to the administration of various psychotropic drugs.