EquiBoost: An Equivariant Boosting Approach to Molecular Conformation Generation (2501.05109v1)

Abstract: Molecular conformation generation plays key roles in computational drug design. Recently developed deep learning methods, particularly diffusion models have reached competitive performance over traditional cheminformatical approaches. However, these methods are often time-consuming or require extra support from traditional methods. We propose EquiBoost, a boosting model that stacks several equivariant graph transformers as weak learners, to iteratively refine 3D conformations of molecules. Without relying on diffusion techniques, EquiBoost balances accuracy and efficiency more effectively than diffusion-based methods. Notably, compared to the previous state-of-the-art diffusion method, EquiBoost improves generation quality and preserves diversity, achieving considerably better precision of Average Minimum RMSD (AMR) on the GEOM datasets. This work rejuvenates boosting and sheds light on its potential to be a robust alternative to diffusion models in certain scenarios.

• The paper introduces EquiBoost, a boosting framework that uses equivariant graph transformers to iteratively refine molecular conformations while maintaining SE(3) invariance.
• It optimizes both local and internal coordinates via a composite loss that includes permutation-invariant RMSD and internal coordinate errors.
• Evaluations on GEOM-QM9 and GEOM-DRUGS show that EquiBoost improves precision and reduces sampling steps, highlighting its potential in computational drug design.

EquiBoost: An Equivariant Boosting Approach to Molecular Conformation Generation

The research paper "EquiBoost: An Equivariant Boosting Approach to Molecular Conformation Generation" introduces a novel method for generating molecular conformations that balances efficiency and accuracy more effectively than existing techniques. This method, termed EquiBoost, employs a boosting framework that integrates several equivariant graph transformers, diverging from the diffusion models prevalently used in the domain.

Summary of Methodology

At the core of EquiBoost is the use of a series of equivariant graph transformers as weak learners in a boosting paradigm. Each learner iteratively refines a conformation, starting from noisy conformations initialized either randomly or through a constrained randomization process using RDKit. The model ensures SE(3) equivariance, which is critical in maintaining the physical invariance properties of molecular structures during transformations. EquiBoost operates directly in Euclidean space, optimizing both local coordinates and internal coordinates, thus handling transformations like rotations and translations effectively.

Training involves optimizing the model by minimizing a composite loss function that includes both Internal Coordinate loss and a permutation-invariant RMSD loss. The latter addresses limitations in traditional RMSD calculations by taking symmetric substructures into account, thus ensuring a more accurate structural alignment.

Evaluation and Results

EquiBoost's performance is validated on the GEOM-QM9 and GEOM-DRUGS datasets, where it demonstrates superior precision and recall compared to both traditional cheminformatics methods and contemporary machine learning approaches. Notably, it surpasses diffusion models such as GeoDiff and Torsional Diffusion in precision metrics, showcasing its ability to produce more accurate molecular conformations with fewer sampling steps. This efficiency in the number of sampling steps enhances its applicability, particularly when computational resources are a concern.

Implications and Future Work

EquiBoost revitalizes the boosting approach within molecular conformation generation, presenting a potentially powerful alternative to the diffusion models that dominate the field. The method's ability to balance generation quality and computational efficiency could have significant implications in computational drug design, where accurate and efficient conformation generation is critical for tasks such as virtual screening and molecular docking.

The constrained randomization technique, leveraging RDKit-initialized conformations, allows EquiBoost to inherit the diversity advantages present in the GEOM dataset, suggesting that it could handle real-world application scenarios effectively. However, additional validation in practical applications, such as molecular docking and other domains requiring generative modeling, remains a promising avenue for further research.

In conclusion, the introduction of EquiBoost marks a notable advancement in molecular conformation generation methodologies, offering a blend of accuracy, efficiency, and robust evaluative metrics that push the boundaries of current deep learning applications in chemistry. Future work can extend its application to more complex systems, potentially broadening its impact across various fields where generative models are employed.