Learning Gradient Fields for Molecular Conformation Generation (2105.03902v3)

Abstract: We study a fundamental problem in computational chemistry known as molecular conformation generation, trying to predict stable 3D structures from 2D molecular graphs. Existing machine learning approaches usually first predict distances between atoms and then generate a 3D structure satisfying the distances, where noise in predicted distances may induce extra errors during 3D coordinate generation. Inspired by the traditional force field methods for molecular dynamics simulation, in this paper, we propose a novel approach called ConfGF by directly estimating the gradient fields of the log density of atomic coordinates. The estimated gradient fields allow directly generating stable conformations via Langevin dynamics. However, the problem is very challenging as the gradient fields are roto-translation equivariant. We notice that estimating the gradient fields of atomic coordinates can be translated to estimating the gradient fields of interatomic distances, and hence develop a novel algorithm based on recent score-based generative models to effectively estimate these gradients. Experimental results across multiple tasks show that ConfGF outperforms previous state-of-the-art baselines by a significant margin.

Learning Gradient Fields for Molecular Conformation Generation

This paper addresses a fundamental problem in computational chemistry: the generation of molecular conformations from 2D molecular graphs. The novel approach introduced, termed ConfGF, proposes estimating the gradient fields of the log density of atomic coordinates directly, leveraging the principles of score-based generative models. This contrasts with traditional methods that estimate interatomic distances first, followed by deriving stable 3D structures, often resulting in compounded errors due to noise in distance predictions.

Methodology

ConfGF aims to exploit the concepts from force field methods used in molecular dynamics simulations. The primary advancement here is the use of gradient fields that allow the generation of stable molecular conformations via Langevin dynamics. The challenge of roto-translation equivariance of the molecular systems is effectively tackled by translating gradient estimates of atomic coordinates into gradients in the interatomic distance space. This is achieved by employing score matching techniques, contributing to a more robust estimation of gradients and consequent generation quality.

Experimental Setup and Results

In evaluating ConfGF, the research employs benchmarks such as GEOM-QM9, GEOM-Drugs, and ISO17 for conformation generation and property prediction tasks. ConfGF demonstrates significant improvement over state-of-the-art baselines across multiple metrics. The conformation generation task shows ConfGF delivering superior diversity (Coverage - COV) and accuracy (Matching - MAT) scores. Moreover, the generation quality is reflected in more accurate ensemble property predictions compared with established methods like RDKit and other learning-based models.

Implications and Future Directions

The practical implications of ConfGF are significant, especially in drug discovery and materials science, where accurate molecular conformations are crucial. The theoretical implications further extend into understanding molecular interactions better, contributing foundational work toward paradigm shifts in computational chemistry practices.

Looking forward, ConfGF lays the groundwork for future developments in AI-driven molecular simulation and design. Integrating stereochemical considerations—a topic not deeply covered here—could provide further refinement to ConfGF and related models. The adaptation of this approach to broader domains such as many-body particle systems remains an open and promising avenue.

Conclusion

ConfGF presents an innovative, single-stage framework that refines the generation of molecular conformations. By directly leveraging atomic coordinates' gradient fields, it surpasses previous models that require multi-step processes, ensuring more efficient and accurate conformation generation. This paper serves as a prominent stepping-stone in the application of neural networks to real-world molecular chemistry problems, reaffirming the interplay between AI and scientific inquiry.