The role of generative artificial intelligence in protein folding

Protein folding is a fundamental process in biology, crucial for understanding cellular functions, disease mechanisms, and drug discovery. Traditional methods, such as molecular dynamics (MD) simulations, have been widely used for protein structure prediction but are often limited by high computational costs and difficulty in exploring complex folding pathways. This study investigates the application of generative artificial intelligence (AI) models —Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models —in protein folding simulation, evaluating their performance in terms of accuracy, efficiency, pathway exploration, and generalizability. The research employed a comprehensive dataset from the Protein Data Bank (PDB) to train and test these models. Results showed that Transformer-based models achieved the highest accuracy, with RMSD values closely matching experimental structures. GANs and VAEs demonstrated significant potential in exploring intermediate folding states, providing new insights into protein folding dynamics. In terms of computational efficiency, generative AI models substantially outperformed traditional MD simulations, making them suitable for large-scale applications. However, challenges were identified, including the need for diverse training datasets to improve model generalizability and the computational demands of Transformer-based models. This study suggests that generative AI offers a scalable, data-driven alternative to traditional protein folding methods, opening new possibilities in structural biology and drug discovery. Further research should focus on optimizing these models and integrating them with experimental data to enhance their predictive power

Keywords:

Protein folding , Generative AI , Structure prediction , Transformer models