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.

6 BIOLOGY AND BIOTECHNOLOGY UDC 314.1 Bachynska Khrystyna Student National University of Life and Environmental Sciences of Ukraine Kyiv, Ukraine NEURAL NETWORKS AND PROTEIN STRUCTURE PREDICTION: THE IMPACT OF ALPHAFOLD Abstract. Protein structure determination is crucial for understanding biological function and advancing drug discovery, but traditional methods are costly and hard to scale. AI has revolutionized this field, with AlphaFold achieving near-experimental accuracy by applying deep learning to amino acid sequences. This paper explores AlphaFold as a paradigm shift in structural biology, highlighting its impact and limitations, notably, its production of static, probabilistic models and emphasis of the need for hybrid approaches combining AI predictions with molecular dynamics and experimental validation. Keywords: AlphaFold, Artificial intelligence (AI), protein folding, structural biology, deep learning, protein structure prediction. AlphaFold represents a paradigm shift in structural biology by transforming protein structure prediction from an experimentally constrained process into a scalable, computationally driven approach; however, its predictions remain inherently