📝 SummarXiv

Abstract

The scarcity of experimental protein-ligand complexes poses a significant challenge for training robust deep learning models for molecular docking. Given the prohibitive cost and time constraints associated with experimental structure determination, scalable generation of realistic protein-ligand complexes is needed to expand available datasets for model development. In this study, we introduce a novel workflow for the procedural generation and validation of synthetic protein-ligand complexes, combining a diverse ensemble of generation techniques and rigorous quality control. We assessed the utility of these synthetic datasets by retraining established docking models, Smina and Gnina, and evaluating their performance on standard benchmarks including the PDBBind core set and the PoseBusters dataset. Our results demonstrate that models trained on synthetic data achieve performance comparable to models trained on experimental data, indicating that current synthetic complexes can effectively capture many salient features of protein-ligand interactions. However, we did not observe significant improvements in docking or scoring accuracy over conventional methods or experimental data augmentation. These findings highlight the promise as well as the current limitations of synthetic data for deep learning-based molecular docking and underscore the need for further refinement in generation methodologies and evaluation strategies to fully exploit the potential of synthetic datasets for this application.