Sampling 3D Molecular Conformers with Diffusion Transformers (2506.15378v1)

Abstract: Diffusion Transformers (DiTs) have demonstrated strong performance in generative modeling, particularly in image synthesis, making them a compelling choice for molecular conformer generation. However, applying DiTs to molecules introduces novel challenges, such as integrating discrete molecular graph information with continuous 3D geometry, handling Euclidean symmetries, and designing conditioning mechanisms that generalize across molecules of varying sizes and structures. We propose DiTMC, a framework that adapts DiTs to address these challenges through a modular architecture that separates the processing of 3D coordinates from conditioning on atomic connectivity. To this end, we introduce two complementary graph-based conditioning strategies that integrate seamlessly with the DiT architecture. These are combined with different attention mechanisms, including both standard non-equivariant and SO(3)-equivariant formulations, enabling flexible control over the trade-off between between accuracy and computational efficiency. Experiments on standard conformer generation benchmarks (GEOM-QM9, -DRUGS, -XL) demonstrate that DiTMC achieves state-of-the-art precision and physical validity. Our results highlight how architectural choices and symmetry priors affect sample quality and efficiency, suggesting promising directions for large-scale generative modeling of molecular structures. Code available at https://github.com/ML4MolSim/dit_mc.

• The paper introduces DiTMC, a novel framework adapting diffusion transformers for generating 3D molecular conformers by integrating graph-based conditioning and attention mechanisms.
• Through experiments, DiTMC achieves state-of-the-art performance on standard benchmarks like GEOM-QM9 and GEOM-DRUGS, generating geometrically realistic conformers with improved precision and validity.
• DiTMC has significant implications for computational drug design and molecular dynamics, offering a robust and scalable tool for generating high-quality molecular conformer ensembles.

Sampling 3D Molecular Conformers with Diffusion Transformers

The paper presents a comprehensive framework, DiTMC (Diffusion Transformer for Molecular Conformer generation), designed to address the critical task of 3D molecular conformer sampling using advanced diffusion transformer models. This work advances state-of-the-art techniques in generative modeling for molecular structures by pioneering the integration of transformer-based architectures with specialized conditioning strategies that respect the unique geometric and chemical properties of molecular data.

Key Contributions

1. Diffusion Transformers for Molecular Data: The authors adapt the general-purpose diffusion transformers (DiTs), which have shown outstanding performance in image synthesis, for application in molecular conformer generation. This adaptation is non-trivial due to the necessity of integrating discrete molecular graph information with continuous 3D geometry.
2. Architectural Innovation: DiTMC introduces a modular architecture that systematically separates the processing of 3D molecular coordinates from atomic connectivity. Key innovations include:
   • Graph-based Conditioning: Incorporation of graph-based conditioning strategies that capture necessary atomic interactions differently, offering flexibility in model design.
   • Attention Mechanisms: Utilization of both standard non-equivariant and SO(3)-equivariant attention mechanisms to balance computational efficiency and accuracy.
3. Superior Performance: Through thorough experimentation, DiTMC achieves state-of-the-art performance on standard conformer generation benchmarks (GEOM-QM9, GEOM-DRUGS, GEOM-XL), demonstrating its efficacy compared to previous methods in generating geometrically realistic conformers.

Experimental Evaluation

Key results indicate that DiTMC markedly improves upon existing models in terms of both precision and physical validity of the generated conformers. Its scalability and performance remain robust even as the model size increases, making it an effective tool for large-scale molecular simulations. Moreover, DiTMC exhibits strong generalization capabilities across diverse molecular datasets, highlighting its practical applicability in areas such as drug discovery and materials science.

The models employing SO(3)-equivariant attention mechanisms offer better accuracy in sample generation, particularly noted in low threshold RMSD scenarios, albeit at a higher computational cost. This suggests a trade-off between incorporating symmetries and computational efficiency, where simpler non-equivariant architectures still manage to achieve high overall performance.

Implications and Future Directions

The proposed DiTMC framework sets the stage for further exploration in the application of transformer-based architectures to molecular and material science. The implications are vast, offering potential improvements in computational drug design and molecular dynamics by providing a robust method for generating high-quality molecular conformer ensembles.

Future research may focus on extending these models to handle larger and more complex molecular systems, potentially incorporating other forms of structural data beyond simple molecular conformers. Additionally, optimizing computational efficiency while maintaining accuracy could facilitate broader adoption of these techniques in practical applications.

In summary, the paper delivers a significant contribution to the field of molecular generative modeling, offering a versatile and powerful tool for sampling 3D molecular conformers with potential implications across various scientific domains.