This study presents a method for predicting the absolute stability of proteins using generative models. The approach leverages a deep learning model called ESM-IF, which was trained on large sequence-structure datasets. The model is used to predict the thermodynamic free energy of unfolding (ΔG) for proteins, which is a key factor in determining protein stability. The method was tested on a benchmark dataset of 265 proteins, with results showing a mean error of 1.5 kcal/mol and a correlation coefficient of 0.7. The model was able to predict the stability of proteins with two-state folding mechanisms, as well as the stability differences between different conformations of proteins. However, the model showed limitations in predicting the stability of larger, more complex proteins where cooperativity between structural elements may play a role. The study also highlights the importance of experimental data in benchmarking and improving the accuracy of predictive models. The results suggest that while the model can predict absolute protein stability with reasonable accuracy, further improvements are needed for larger and more complex systems. The study also compares the performance of ESM-IF with other models, including sequence-based language models and energy evaluation methods like FoldX. Overall, the study demonstrates the potential of generative models in predicting protein stability and highlights the challenges in accurately predicting stability for complex proteins. The results suggest that while the model can predict absolute stability with reasonable accuracy, further improvements are needed for larger and more complex systems. The study also highlights the importance of experimental data in benchmarking and improving the accuracy of predictive models.This study presents a method for predicting the absolute stability of proteins using generative models. The approach leverages a deep learning model called ESM-IF, which was trained on large sequence-structure datasets. The model is used to predict the thermodynamic free energy of unfolding (ΔG) for proteins, which is a key factor in determining protein stability. The method was tested on a benchmark dataset of 265 proteins, with results showing a mean error of 1.5 kcal/mol and a correlation coefficient of 0.7. The model was able to predict the stability of proteins with two-state folding mechanisms, as well as the stability differences between different conformations of proteins. However, the model showed limitations in predicting the stability of larger, more complex proteins where cooperativity between structural elements may play a role. The study also highlights the importance of experimental data in benchmarking and improving the accuracy of predictive models. The results suggest that while the model can predict absolute protein stability with reasonable accuracy, further improvements are needed for larger and more complex systems. The study also compares the performance of ESM-IF with other models, including sequence-based language models and energy evaluation methods like FoldX. Overall, the study demonstrates the potential of generative models in predicting protein stability and highlights the challenges in accurately predicting stability for complex proteins. The results suggest that while the model can predict absolute stability with reasonable accuracy, further improvements are needed for larger and more complex systems. The study also highlights the importance of experimental data in benchmarking and improving the accuracy of predictive models.