PDCG: A Diffusion Model Guided by Pre-Training for Molecular Conformation Generation

Background: While machine learning has advanced molecular conformation generation, existing models often suffer from limited generalization and inaccuracies, especially for complex molecular structures. These limitations hinder their reliability in downstream applications. Methods: We proposed a molecular conformation model combined with a molecular graph pre-training module and a diffusion model (PDCG). Feature embeddings are obtained from a pre-trained model and concatenated with the molecular graph information. Fusion features are used for generating conformations in the model. The model was trained and evaluated on the GEOM-QM9 and GEOM-Drugs datasets. Results: PDCG significantly outperforms existing baselines, which shows markedly superior results. Furthermore, in downstream molecular property prediction tasks, conformations generated by PDCG yield results comparable to those derived from DFT-optimized geometries. Conclusions: Our work provides a robust and generalizable model for accurate conformation generation. PDCG offers a reliable tool for downstream computational tasks, such as the virtual screening of functional materials and drug-like molecules.