Artificial neural networks (ANNs) have been applied for a wide range of QSAR tasks. Increase in the use of RF and Support Vector Machines for classification and regression in cheminformatics led to a decline in the use of ANNs. Eventually, the ANNs have evolved into DNNs. Unlike ANNs, a DNN consists of multiple fully connected layers with two or more hidden (or intermediate) layers between the input and output layers. In a feedforward neural network (referred simply as DNN in the rest of the study), the information passed through the input layer flows in forward direction through the hidden layers to the output layer. A number of parameters are available for tuning a DNN such as the number of hidden layers, number of epochs, activation function, optimizer and its learning rate. Hyperparameter optimization is essential to improve the performance of DNN and avoid overfitting on training data. This is detailed in the Model Optimization section of the results.