Bioscience Method 2024, Vol.15, No.1, 28-36 http://bioscipublisher.com/index.php/bm 29 Furthermore, this study will also discuss the challenges and future directions of applying deep learning in the field of drug discovery, aiming to provide valuable insights and guidance for drug researchers and developers. Through this research, it is expected to drive innovation in the field of drug discovery, bring more effective and safer new therapies to patients, provide a solid foundation for the further application and development of deep learning in drug discovery, and inspire more researchers and developers to explore this promising field. 1 Overview of Deep Learning Methods 1.1 Convolutional neural networks (CNN) Convolutional Neural Networks (CNNs) are a fundamental and widely used network architecture within deep learning technologies, particularly suited for image processing and recognition tasks. In the field of drug discovery, CNNs are employed to process molecular images and structural data to identify and predict the biological activity of compounds. Molecular structures can be represented visually, where different atoms and chemical bonds are depicted using various colors and shapes. CNNs can extract important features from these images through their convolutional layers, learning about the molecule's intrinsic properties and activity correlations. This approach has shown superior performance in predicting drug molecule solubility, toxicity, and affinity towards specific proteins. Jones et al. (2020) introduced a hybrid model that combines features from different representations, such as three-dimensional CNNs (3D-CNNs) and spatial graph CNNs (SG-CNNs), to enhance the accuracy of binding affinity predictions. They compared the performance of these models with traditional methods, demonstrating that their hybrid model surpasses both individual neural network models and conventional scoring methods in terms of accuracy and computational efficiency. Improving the prediction precision of protein-ligand binding affinity is crucial in drug discovery. Hentabli et al. (2022) focused on developing a deep learning approach to predict the biological activity of compounds, introducing a novel technique using a convolutional neural network (CNN) model. The model was evaluated using standard datasets with homogenous and heterogenous activity categories (MDL Drug Data Report and Sutherland). By leveraging deep learning techniques, they advanced the computational prediction of compound biological activity, which is vital for the drug discovery and development process. Yaseen (2023) proposed an innovative method using artificial intelligence, particularly convolutional neural networks (CNNs), to predict drug-target interactions (DTIs). This study developed a system based on machine learning and deep learning to classify drug-target interactions of different drug combinations. The results indicated that the new method significantly enhances the prediction accuracy of DTIs, which could accelerate the drug discovery and development processes. These examples underscore the transformative impact of CNNs in the realm of drug discovery, highlighting their capability to significantly refine the prediction of molecular activities and interactions. 1.2 Recurrent neural networks (RNN) Recurrent Neural Networks (RNNs) are another type of deep learning model, designed to process sequential data such as text or time series. In drug discovery, RNNs are utilized to handle molecular sequences, specifically the one-dimensional Simplified Molecular Input Line Entry System (SMILES) representations of compounds. By leveraging RNNs to process these sequences, researchers can capture long-term dependencies and patterns in molecular structures, thereby predicting the biological activities of molecules. RNNs are particularly adept at managing dynamically lengthed molecular sequences, learning complex chemical information from intricate molecular structures. Zhang et al. (2019) introduced a deep learning-based method, DLBSS, which predicts transcription factor binding sites (TFBSs) by integrating DNA sequences with DNA shape features. This method employs a shared Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to discover common patterns from DNA sequences and their corresponding shape characteristics. Amabilino et al. (2020) discovered that Recurrent Neural Networks (RNNs) could serve as SMILES string
RkJQdWJsaXNoZXIy MjQ4ODYzMg==