Proceedings of the International scientific and practical conference “Science in the Modern World” (January 19-21, 2026) / Publisher website: www.naukainfo.com. - Cambridge, United Kingdom, 2026. - 203 p.

10 absence of strong co-evolutionary signals [2, pp. 6280-6281]. In these cases, AlphaFold cannot leverage its learned evolutionary correlations and instead reveals its limited ability to infer structure from biophysical principles alone. While extensions toward multimer prediction have expanded AlphaFold’s scope, accurately modeling large protein assemblies, flexible interfaces, transient interactions, and the structural consequences of post-translational modifications remains challenging. These limitations reinforce the need for hybrid approaches in which AlphaFold predictions serve as probabilistic starting models that are refined through molecular dynamics simulations and validated by experimental methods, bridging the gap between static prediction and dynamic biological reality [1, pp. 10- 12]. The future of AI-driven structural biology lies in hybrid approaches that integrate AlphaFold predictions with experimental data and physics-based simulations. Advances in AI architectures and training datasets are expected to improve modeling of protein complexes, post-translational modifications, and protein-ligand interactions [1, pp. 12-14]. Rather than replacing experimental structural biology, AlphaFold redefines its role by shifting experimental efforts toward validation, conformational dynamics, and the characterization of complex assemblies under physiological conditions. This reconfiguration marks a new era in which computational and experimental methods operate synergistically to accelerate discovery [4, p. 1890]. AlphaFold has fundamentally altered the landscape of protein structure prediction by removing long-standing experimental bottlenecks and enabling large- scale, high-accuracy computational modeling. While its limitations underscore the continued necessity of experimental and dynamic approaches, AlphaFold represents a decisive step toward a computationally driven paradigm in biotechnology and structural biology.