From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning
This addresses a key bottleneck in structure-based drug design by improving binding affinity prediction accuracy, though it's an incremental advance over existing data-driven methods.
The researchers tackled the problem of inaccurate protein-ligand binding affinity prediction by developing Dynaformer, a graph-based deep learning model that uses molecular dynamics simulation data instead of static structures. The model achieved state-of-the-art performance on the CASF-2016 benchmark and identified 12 experimentally validated hit compounds in a virtual screening test.
Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally determined by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, an MD dataset containing 3,218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories. In silico experiments demonstrated that the model exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, in a virtual screening on heat shock protein 90 (HSP90) using Dynaformer, 20 candidates are identified and their binding affinities are further experimentally validated. Dynaformer displayed promising results in virtual drug screening, revealing 12 hit compounds (two are in the submicromolar range), including several novel scaffolds. Overall, these results demonstrated that the approach offer a promising avenue for accelerating the early drug discovery process.