**1. Introduction**

Artificial neural networks (ANN) contain a set of parameters that can be adjusted to perform different tasks. These structures have universal approximation properties, which means that they can approximate any function in any size and, generally, up to a desired degree of accuracy [1–4].

In this article, we present a series of deep learning training and optimization strategies that have been applied to improve the performance of an ANN identifying JWH-syntheticcannabinoid-class membership. In order to increase the system sensitivity, we trained and optimized an initial model on four new architectures. For this purpose, we used the data science and machine learning platform *Neural Designer.* The best version was implemented in the *Python 3.10* programming language for further development and improvement.

The classification efficiencies (output results) obtained for several combinations of algorithms, error parameters and regularization methods were compared. The good fit between the test samples and the corresponding ANN outputs was also analyzed. The effectiveness of the methods was analyzed and is presented in detail.
