Contemporary achievements in the field of artificial intelligence are radically transforming approaches to the study of complex biological systems, in particular through the application of deep neural networks for predicting the spatial structure of protein molecules based on their amino acid sequences, which opens new prospects in understanding the functional aspects of proteomics and interactions at the molecular level. The integration of convolutional and transformer architectures within advanced algorithms ensures high accuracy in modeling intramolecular interactions, including hydrogen bonds, hydrophobic effects, and electrostatic forces, which allows for the identification of functional domains, potential ligand-binding sites, and allosteric regulatory centers with minimal costs associated with labor-intensive experimental research using crystallographic or spectroscopic methods. Particular attention in the analysis is given to the mechanisms of training on extensive sets of structural data accumulated through X-ray structural analysis, nuclear magnetic resonance, and cryo-electron microscopy, where the optimization of loss functions through regularization and data augmentation techniques contributes to minimizing errors in predicting tertiary and quaternary structures, as well as dynamic transitions between conformations. The challenges associated with generalizing models to previously uncovered protein families are discussed, including the consideration of evolutionarily conserved motifs, post-translational modifications, and the influence of the solvent on folding stability under conditions of physiological temperatures and pH. The development of multimodal approaches that combine sequential data with information on the physicochemical properties of amino acids, energy profiles, and coevolutionary signals from multiple sequence alignments demonstrates a significant increase in performance compared to traditional methods of homology modeling and ab initio calculations. In the context of pharmacological design and the development of biotechnological products, such technologies substantially accelerate the process of virtual screening of potential therapeutic agents, facilitating the identification of compounds with high affinity, selectivity, and optimal pharmacokinetic characteristics for target proteins involved in the pathogenesis of neurodegenerative, oncological, and infectious diseases. The analysis of the limitations of current models emphasizes the need to account for context-dependent factors, such as chaperone-mediated folding, interaction with membranes, and the influence of cellular crowding on folding kinetics. Prospects for further improvement are associated with the implementation of generative adversarial networks and diffusion models capable not only of predicting existing structures but also of designing de novo protein sequences with predetermined catalytic or structural properties, which opens broad horizons for synthetic biology, enzyme design, and the creation of new protein-based materials. The presented concepts and analytical conclusions illustrate how the combination of computational paradigms of machine learning with experimental validation through biophysics methods forms a solid foundation for breakthrough discoveries in molecular medicine and biotechnology, providing specialists with an effective tool for the rapid assessment of the relevance of detailed studies in this dynamically developing area of interdisciplinary science, identifying key innovations in methodology, and making informed decisions regarding the need to refer to the full text to obtain an in-depth understanding of technical details and potential applications.