Quantification of the Impact of Structure Quality on Predicted Binding Free Energy Accuracy

Relative binding free energy (RBFE) calculations have emerged as a powerful tool in drug discovery, capable of achieving experimental-level accuracy. However, the accuracy is compromised by a multitude of factors, including the initial structure modelling. The current study contributes to the quantification of the impact of initial structure modelling on the accuracy across a diverse set of activity cliff pairs. Along with providing a quantitative relation between the resolution of the crystal structure and free energy accuracy, we also demonstrate the incorporation of a secondary solvation tool (SOLVATE) to increase the free energy accuracy, especially when crystal waters are missing. The study also evaluates the reliability of AI-predicted structures in RBFE calculations, showing their effectiveness in predicting RBFE directionality and assigning nominal resolutions to the predicted structures based on free energy accuracy. These findings have significant implications for the development of more robust RBFE protocols, informing the use of structural data, solvation techniques, and AI-predicted protein models in drug discovery.