Artificial neural networks (ANNs) have emerged as a valuable tool in facilitating the design of synthesis and guiding subsequent biological experiments in the systematic exploration for novel antimicrobial agents. In this paper, two multilayer perceptron-type neural networks (MLP) are designed to predict the biological activity of compounds based on their physicochemical properties and structure. This approach was tested against Enterococcus faecalis using a series of 140 imidazole derivatives. The use of quaternary ammonium salts in this research originated from their acknowledged ability to act as antiseptics and disinfectants. Additionally, they were considered promising in addressing various microorganisms, including Gram-positive bacteria. The designed regression model accurately predicted the minimum inhibitory concentration for E. faecalis growth. The coefficient of correlation between the actual values and the network predictions for the training set was R=0.91, for the test set was R=0.91, and for the validation set was R=0.97.Additionally, the classification model successfully categorized the tested compounds as predictively active or inactive against the targeted microorganism (classification accuracy: 92.86%). Sensitivity analyses highlighted specific molecular descriptors derived from the Molecular Properties block, such as log P, refractive index, molecular weight, and atom count, as pivotal factors influencing model construction. In summary, the above-mentioned discoveries emphasize the practicality of Artificial Neural Network models in forecasting the antibacterial effectiveness of quaternary ammonium salts against E. faecalis. The application of ANNs in data analysis allows for efficient optimization and cost reduction by streamlining the compound synthesis process towards achieving the desired properties. By harnessing the computational power of ANNs, researchers can effectively narrow down the pool of potential compounds, thereby expediting the discovery of promising antimicrobial substances.