Classifying Molecule Categories Using Deep Neural Networks

The analysis and classification of molecules such as proteins are essential in biology and medicine. These tasks pose considerable challenges due to the complexity of molecular structures and the variability of their sizes. Traditional methods, while sophisticated, rely heavily on human expertise and are limited in their ability to deal effectively with these challenges. Recent advances in Deep Learning offer promising solutions, exploiting the capabilities of multi-layer artificial neural networks to extract complex relationships from molecular structural data. This project aims to develop a system for classifying molecular images with high accuracy, using advanced Deep Learning techniques. Convolutional neural network (CNN) architectures VGG16 and ResNet are used here to optimize the performance of the classification model. By comparing the performance of models trained on the original dataset with those trained on the augmented dataset, we aim to demonstrate the effectiveness of data augmentation in enhancing model accuracy. The main objectives are to increase the accuracy of the classification model and to validate the method using independent datasets. By optimizing these CNN architectures and using regularization techniques, aiming to improve the accuracy of molecule classification. Expected results include a better understanding of the patterns and discriminating features of different classes of molecules, facilitating the study of fundamental biological processes, the identification of biomarkers for disease diagnosis and prognosis, and the search for new therapeutic targets. By leveraging advanced Deep Learning techniques, we aim to make significant strides in the field of molecular biology, offering new tools and methodologies for researchers and clinicians alike, thus advancing both scientific research and medical practice. By providing valuable insights for research in molecular biology and medicine, this approach has the potential to revolutionize the way we understand and utilize molecular data in various scientific and medical fields.